Instruction: What field is the article from?

Input:
Title: Churn Prediction via Multimodal Fusion Learning:Integrating Customer Financial Literacy, Voice, and Behavioral Data
Abstract: In todays competitive landscape, businesses grapple with customer retention.
Churn prediction models, although beneficial, often lack accuracy due to the
reliance on a single data source. The intricate nature of human behavior and
high dimensional customer data further complicate these efforts. To address
these concerns, this paper proposes a multimodal fusion learning model for
identifying customer churn risk levels in financial service providers. Our
multimodal approach integrates customer sentiments financial literacy (FL)
level, and financial behavioral data, enabling more accurate and bias-free
churn prediction models. The proposed FL model utilizes a SMOGN COREG
supervised model to gauge customer FL levels from their financial data. The
baseline churn model applies an ensemble artificial neural network and
oversampling techniques to predict churn propensity in high-dimensional
financial data. We also incorporate a speech emotion recognition model
employing a pre-trained CNN-VGG16 to recognize customer emotions based on
pitch, energy, and tone. To integrate these diverse features while retaining
unique insights, we introduced late and hybrid fusion techniques that
complementary boost coordinated multimodal co learning. Robust metrics were
utilized to evaluate the proposed multimodal fusion model and hence the
approach validity, including mean average precision and macro-averaged F1
score. Our novel approach demonstrates a marked improvement in churn
prediction, achieving a test accuracy of 91.2%, a Mean Average Precision (MAP)
score of 66, and a Macro-Averaged F1 score of 54 through the proposed hybrid
fusion learning technique compared with late fusion and baseline models.
Furthermore, the analysis demonstrates a positive correlation between negative
emotions, low FL scores, and high-risk customers.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: XplainLLM: A QA Explanation Dataset for Understanding LLM Decision-Making
Abstract: Large Language Models (LLMs) have recently made impressive strides in natural
language understanding tasks. Despite their remarkable performance,
understanding their decision-making process remains a big challenge. In this
paper, we look into bringing some transparency to this process by introducing a
new explanation dataset for question answering (QA) tasks that integrates
knowledge graphs (KGs) in a novel way. Our dataset includes 12,102
question-answer-explanation (QAE) triples. Each explanation in the dataset
links the LLM's reasoning to entities and relations in the KGs. The explanation
component includes a why-choose explanation, a why-not-choose explanation, and
a set of reason-elements that underlie the LLM's decision. We leverage KGs and
graph attention networks (GAT) to find the reason-elements and transform them
into why-choose and why-not-choose explanations that are comprehensible to
humans. Through quantitative and qualitative evaluations, we demonstrate the
potential of our dataset to improve the in-context learning of LLMs, and
enhance their interpretability and explainability. Our work contributes to the
field of explainable AI by enabling a deeper understanding of the LLMs
decision-making process to make them more transparent and thereby, potentially
more reliable, to researchers and practitioners alike. Our dataset is available
at: https://github.com/chen-zichen/XplainLLM_dataset.git

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Using GPT-4 to Augment Unbalanced Data for Automatic Scoring
Abstract: Machine learning-based automatic scoring can be challenging if students'
responses are unbalanced across scoring categories, as it introduces
uncertainty in the machine training process. To meet this challenge, we
introduce a novel text data augmentation framework using GPT-4, a generative
large language model, specifically tailored for unbalanced datasets in
automatic scoring. Our experimental dataset comprised student-written responses
to two science items. We crafted prompts for GPT-4 to generate responses
resembling student-written answers, particularly for the minority scoring
classes, to augment the data. We then finetuned DistillBERT for automatic
scoring based on the augmented and original datasets. Model performance was
assessed using accuracy, precision, recall, and F1 score. We incorporate varied
amounts of augmented data to examine scoring performance, and our findings
revealed remarkedly improved model performance. The average maximum increase
observed across two items is: 3.5% for accuracy, 30.6% for precision, 21.1% for
recall, and 24.2% for F1 score. Notably, using just 5% of the augmented data
led to substantial improvements: 2.6%, 29.2%, 15.1%, and 19.6%. Interestingly,
the extent of improvement varied depending on specific datasets. Moreover, we
found that a varying amount of augmented data (5%-40%) was needed to obtain a
stable improvement. We also compare models trained with GPT-4 augmented data
and those trained with additional student-written responses. The findings
indicate that former ones match or even exceed the performance of the latter.
Specifically, there is an average difference of 1.7%, 1.9%, 11.0%, and 7.8% for
four metrics separately. This research underscores the potential and
effectiveness of data augmentation techniques utilizing GPT-4 in addressing
unbalanced datasets within automated assessment.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: ProAgent: From Robotic Process Automation to Agentic Process Automation
Abstract: From ancient water wheels to robotic process automation (RPA), automation
technology has evolved throughout history to liberate human beings from arduous
tasks. Yet, RPA struggles with tasks needing human-like intelligence,
especially in elaborate design of workflow construction and dynamic
decision-making in workflow execution. As Large Language Models (LLMs) have
emerged human-like intelligence, this paper introduces Agentic Process
Automation (APA), a groundbreaking automation paradigm using LLM-based agents
for advanced automation by offloading the human labor to agents associated with
construction and execution. We then instantiate ProAgent, an LLM-based agent
designed to craft workflows from human instructions and make intricate
decisions by coordinating specialized agents. Empirical experiments are
conducted to detail its construction and execution procedure of workflow,
showcasing the feasibility of APA, unveiling the possibility of a new paradigm
of automation driven by agents. Our code is public at
https://github.com/OpenBMB/ProAgent.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: SigFormer: Sparse Signal-Guided Transformer for Multi-Modal Human Action Segmentation
Abstract: Multi-modal human action segmentation is a critical and challenging task with
a wide range of applications. Nowadays, the majority of approaches concentrate
on the fusion of dense signals (i.e., RGB, optical flow, and depth maps).
However, the potential contributions of sparse IoT sensor signals, which can be
crucial for achieving accurate recognition, have not been fully explored. To
make up for this, we introduce a Sparse signalguided Transformer (SigFormer) to
combine both dense and sparse signals. We employ mask attention to fuse
localized features by constraining cross-attention within the regions where
sparse signals are valid. However, since sparse signals are discrete, they lack
sufficient information about the temporal action boundaries. Therefore, in
SigFormer, we propose to emphasize the boundary information at two stages to
alleviate this problem. In the first feature extraction stage, we introduce an
intermediate bottleneck module to jointly learn both category and boundary
features of each dense modality through the inner loss functions. After the
fusion of dense modalities and sparse signals, we then devise a two-branch
architecture that explicitly models the interrelationship between action
category and temporal boundary. Experimental results demonstrate that SigFormer
outperforms the state-of-the-art approaches on a multi-modal action
segmentation dataset from real industrial environments, reaching an outstanding
F1 score of 0.958. The codes and pre-trained models have been available at
https://github.com/LIUQI-creat/SigFormer.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Deep Natural Language Feature Learning for Interpretable Prediction
Abstract: We propose a general method to break down a main complex task into a set of
intermediary easier sub-tasks, which are formulated in natural language as
binary questions related to the final target task. Our method allows for
representing each example by a vector consisting of the answers to these
questions. We call this representation Natural Language Learned Features
(NLLF). NLLF is generated by a small transformer language model (e.g., BERT)
that has been trained in a Natural Language Inference (NLI) fashion, using weak
labels automatically obtained from a Large Language Model (LLM). We show that
the LLM normally struggles for the main task using in-context learning, but can
handle these easiest subtasks and produce useful weak labels to train a BERT.
The NLI-like training of the BERT allows for tackling zero-shot inference with
any binary question, and not necessarily the ones seen during the training. We
show that this NLLF vector not only helps to reach better performances by
enhancing any classifier, but that it can be used as input of an
easy-to-interpret machine learning model like a decision tree. This decision
tree is interpretable but also reaches high performances, surpassing those of a
pre-trained transformer in some cases.We have successfully applied this method
to two completely different tasks: detecting incoherence in students' answers
to open-ended mathematics exam questions, and screening abstracts for a
systematic literature review of scientific papers on climate change and
agroecology.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: The Expressive Power of Low-Rank Adaptation
Abstract: Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method that
leverages low-rank adaptation of weight matrices, has emerged as a prevalent
technique for fine-tuning pre-trained models such as large language models and
diffusion models. Despite its huge success in practice, the theoretical
underpinnings of LoRA have largely remained unexplored. This paper takes the
first step to bridge this gap by theoretically analyzing the expressive power
of LoRA. We prove that, for fully connected neural networks, LoRA can adapt any
model $f$ to accurately represent any smaller target model $\overline{f}$ if
LoRA-rank $\geq(\text{width of }f) \times \frac{\text{depth of
}\overline{f}}{\text{depth of }f}$. We also quantify the approximation error
when LoRA-rank is lower than the threshold. For Transformer networks, we show
any model can be adapted to a target model of the same size with
rank-$(\frac{\text{embedding size}}{2})$ LoRA adapters.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning
Abstract: Federated learning (FL) emphasizes decentralized training by storing data
locally and sending only model updates, underlining user privacy. Recently, a
line of works on privacy attacks impairs user privacy by extracting sensitive
training text from language models in the context of FL. Yet, these attack
techniques face distinct hurdles: some work chiefly with limited batch sizes
(e.g., batch size of 1), and others are easily detectable. This paper
introduces an innovative approach that is challenging to detect, significantly
enhancing the recovery rate of text in various batch-size settings. Building on
fundamental gradient matching and domain prior knowledge, we enhance the attack
by recovering the input of the Pooler layer of language models, which enables
us to provide additional supervised signals at the feature level. Unlike
gradient data, these signals do not average across sentences and tokens,
thereby offering more nuanced and effective insights. We benchmark our method
using text classification tasks on datasets such as CoLA, SST-2, and Rotten
Tomatoes. Across different batch sizes and models, our approach consistently
outperforms previous state-of-the-art results.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs
Abstract: This paper presents an in-depth study of multimodal machine translation
(MMT), examining the prevailing understanding that MMT systems exhibit
decreased sensitivity to visual information when text inputs are complete.
Instead, we attribute this phenomenon to insufficient cross-modal interaction,
rather than image information redundancy. A novel approach is proposed to
generate parallel Visual Question-Answering (VQA) style pairs from the source
text, fostering more robust cross-modal interaction. Using Large Language
Models (LLMs), we explicitly model the probing signal in MMT to convert it into
VQA-style data to create the Multi30K-VQA dataset. An MMT-VQA multitask
learning framework is introduced to incorporate explicit probing signals from
the dataset into the MMT training process. Experimental results on two
widely-used benchmarks demonstrate the effectiveness of this novel approach.
Our code and data would be available at:
\url{https://github.com/libeineu/MMT-VQA}.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models
Abstract: A new method called the Survival Beran-based Neural Importance Model
(SurvBeNIM) is proposed. It aims to explain predictions of machine learning
survival models, which are in the form of survival or cumulative hazard
functions. The main idea behind SurvBeNIM is to extend the Beran estimator by
incorporating the importance functions into its kernels and by implementing
these importance functions as a set of neural networks which are jointly
trained in an end-to-end manner. Two strategies of using and training the whole
neural network implementing SurvBeNIM are proposed. The first one explains a
single instance, and the neural network is trained for each explained instance.
According to the second strategy, the neural network only learns once on all
instances from the dataset and on all generated instances. Then the neural
network is used to explain any instance in a dataset domain. Various numerical
experiments compare the method with different existing explanation methods. A
code implementing the proposed method is publicly available.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Search-Based Fairness Testing: An Overview
Abstract: Artificial Intelligence (AI) has demonstrated remarkable capabilities in
domains such as recruitment, finance, healthcare, and the judiciary. However,
biases in AI systems raise ethical and societal concerns, emphasizing the need
for effective fairness testing methods. This paper reviews current research on
fairness testing, particularly its application through search-based testing.
Our analysis highlights progress and identifies areas of improvement in
addressing AI systems biases. Future research should focus on leveraging
established search-based testing methodologies for fairness testing.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Vertical Federated Alzheimer's Detection on Multimodal Data
Abstract: In the era of rapidly advancing medical technologies, the segmentation of
medical data has become inevitable, necessitating the development of privacy
preserving machine learning algorithms that can train on distributed data.
Consolidating sensitive medical data is not always an option particularly due
to the stringent privacy regulations imposed by the Health Insurance
Portability and Accountability Act (HIPAA). In this paper, we introduce a HIPAA
compliant framework that can train from distributed data. We then propose a
multimodal vertical federated model for Alzheimer's Disease (AD) detection, a
serious neurodegenerative condition that can cause dementia, severely impairing
brain function and hindering simple tasks, especially without preventative
care. This vertical federated model offers a distributed architecture that
enables collaborative learning across diverse sources of medical data while
respecting privacy constraints imposed by HIPAA. It is also able to leverage
multiple modalities of data, enhancing the robustness and accuracy of AD
detection. Our proposed model not only contributes to the advancement of
federated learning techniques but also holds promise for overcoming the hurdles
posed by data segmentation in medical research. By using vertical federated
learning, this research strives to provide a framework that enables healthcare
institutions to harness the collective intelligence embedded in their
distributed datasets without compromising patient privacy.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Algorithmic Transparency and Manipulation
Abstract: A series of recent papers raises worries about the manipulative potential of
algorithmic transparency. But while the concern is apt and relevant, it is
based on a fraught understanding of manipulation. Therefore, this paper draws
attention to the indifference view of manipulation, which explains better than
the vulnerability view why algorithmic transparency has manipulative potential.
The paper also raises pertinent research questions for future studies of
manipulation in the context of algorithmic transparency.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts
Abstract: Graph neural networks (GNNs) have led to major breakthroughs in a variety of
domains such as drug discovery, social network analysis, and travel time
estimation. However, they lack interpretability which hinders human trust and
thereby deployment to settings with high-stakes decisions. A line of
interpretable methods approach this by discovering a small set of relevant
concepts as subgraphs in the last GNN layer that together explain the
prediction. This can yield oversimplified explanations, failing to explain the
interaction between GNN layers. To address this oversight, we provide HELP
(Hierarchical Explainable Latent Pooling), a novel, inherently interpretable
graph pooling approach that reveals how concepts from different GNN layers
compose to new ones in later steps. HELP is more than 1-WL expressive and is
the first non-spectral, end-to-end-learnable, hierarchical graph pooling method
that can learn to pool a variable number of arbitrary connected components. We
empirically demonstrate that it performs on-par with standard GCNs and popular
pooling methods in terms of accuracy while yielding explanations that are
aligned with expert knowledge in the domains of chemistry and social networks.
In addition to a qualitative analysis, we employ concept completeness scores as
well as concept conformity, a novel metric to measure the noise in discovered
concepts, quantitatively verifying that the discovered concepts are
significantly easier to fully understand than those from previous work. Our
work represents a first step towards an understanding of graph neural networks
that goes beyond a set of concepts from the final layer and instead explains
the complex interplay of concepts on different levels.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning
Abstract: In maritime traffic surveillance, detecting illegal activities, such as
illegal fishing or transshipment of illicit products is a crucial task of the
coastal administration. In the open sea, one has to rely on Automatic
Identification System (AIS) message transmitted by on-board transponders, which
are captured by surveillance satellites. However, insincere vessels often
intentionally shut down their AIS transponders to hide illegal activities. In
the open sea, it is very challenging to differentiate intentional AIS shutdowns
from missing reception due to protocol limitations, bad weather conditions or
restricting satellite positions. This paper presents a novel approach for the
detection of abnormal AIS missing reception based on self-supervised deep
learning techniques and transformer models. Using historical data, the trained
model predicts if a message should be received in the upcoming minute or not.
Afterwards, the model reports on detected anomalies by comparing the prediction
with what actually happens. Our method can process AIS messages in real-time,
in particular, more than 500 Millions AIS messages per month, corresponding to
the trajectories of more than 60 000 ships. The method is evaluated on 1-year
of real-world data coming from four Norwegian surveillance satellites. Using
related research results, we validated our method by rediscovering already
detected intentional AIS shutdowns.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Improving search relevance of Azure Cognitive Search by Bayesian optimization
Abstract: Azure Cognitive Search (ACS) has emerged as a major contender in "Search as a
Service" cloud products in recent years. However, one of the major challenges
for ACS users is to improve the relevance of the search results for their
specific usecases. In this paper, we propose a novel method to find the optimal
ACS configuration that maximizes search relevance for a specific usecase
(product search, document search...) The proposed solution improves key online
marketplace metrics such as click through rates (CTR) by formulating the search
relevance problem as hyperparameter tuning. We have observed significant
improvements in real-world search call to action (CTA) rate in multiple
marketplaces by introducing optimized weights generated from the proposed
approach.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Evaluating Large Language Models for Health-related Queries with Presuppositions
Abstract: As corporations rush to integrate large language models (LLMs) to their
search offerings, it is critical that they provide factually accurate
information that is robust to any presuppositions that a user may express. In
this work, we introduce UPHILL, a dataset consisting of health-related queries
with varying degrees of presuppositions. Using UPHILL, we evaluate the factual
accuracy and consistency of InstructGPT, ChatGPT, and BingChat models. We find
that while model responses rarely disagree with true health claims (posed as
questions), they often fail to challenge false claims: responses from
InstructGPT agree with 32% of the false claims, ChatGPT 26% and BingChat 23%.
As we increase the extent of presupposition in input queries, the responses
from InstructGPT and ChatGPT agree with the claim considerably more often,
regardless of its veracity. Responses from BingChat, which rely on retrieved
webpages, are not as susceptible. Given the moderate factual accuracy, and the
inability of models to consistently correct false assumptions, our work calls
for a careful assessment of current LLMs for use in high-stakes scenarios.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias
Abstract: Large language models (LLMs) have demonstrated their potential in social
science research by emulating human perceptions and behaviors, a concept
referred to as algorithmic fidelity. This study assesses the algorithmic
fidelity and bias of LLMs by utilizing two nationally representative climate
change surveys. The LLMs were conditioned on demographics and/or psychological
covariates to simulate survey responses. The findings indicate that LLMs can
effectively capture presidential voting behaviors but encounter challenges in
accurately representing global warming perspectives when relevant covariates
are not included. GPT-4 exhibits improved performance when conditioned on both
demographics and covariates. However, disparities emerge in LLM estimations of
the views of certain groups, with LLMs tending to underestimate worry about
global warming among Black Americans. While highlighting the potential of LLMs
to aid social science research, these results underscore the importance of
meticulous conditioning, model selection, survey question format, and bias
assessment when employing LLMs for survey simulation. Further investigation
into prompt engineering and algorithm auditing is essential to harness the
power of LLMs while addressing their inherent limitations.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: On the verification of Embeddings using Hybrid Markov Logic
Abstract: The standard approach to verify representations learned by Deep Neural
Networks is to use them in specific tasks such as classification or regression,
and measure their performance based on accuracy in such tasks. However, in many
cases, we would want to verify more complex properties of a learned
representation. To do this, we propose a framework based on a probabilistic
first-order language, namely, Hybrid Markov Logic Networks (HMLNs) where we
specify properties over embeddings mixed with symbolic domain knowledge. We
present an approach to learn parameters for the properties within this
framework. Further, we develop a verification method to test embeddings in this
framework by encoding this task as a Mixed Integer Linear Program for which we
can leverage existing state-of-the-art solvers. We illustrate verification in
Graph Neural Networks, Deep Knowledge Tracing and Intelligent Tutoring Systems
to demonstrate the generality of our approach.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
Abstract: Contrastive Language-Image Pre-training (CLIP) plays an essential role in
extracting valuable content information from images across diverse tasks. It
aligns textual and visual modalities to comprehend the entire image, including
all the details, even those irrelevant to specific tasks. However, for a finer
understanding and controlled editing of images, it becomes crucial to focus on
specific regions of interest, which can be indicated as points, masks, or boxes
by humans or perception models. To fulfill the requirements, we introduce
Alpha-CLIP, an enhanced version of CLIP with an auxiliary alpha channel to
suggest attentive regions and fine-tuned with constructed millions of RGBA
region-text pairs. Alpha-CLIP not only preserves the visual recognition ability
of CLIP but also enables precise control over the emphasis of image contents.
It demonstrates effectiveness in various tasks, including but not limited to
open-world recognition, multimodal large language models, and conditional 2D /
3D generation. It has a strong potential to serve as a versatile tool for
image-related tasks.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Heuristics-Driven Link-of-Analogy Prompting: Enhancing Large Language Models for Document-Level Event Argument Extraction
Abstract: In this study, we investigate in-context learning (ICL) in document-level
event argument extraction (EAE). The paper identifies key challenges in this
problem, including example selection, context length limitation, abundance of
event types, and the limitation of Chain-of-Thought (CoT) prompting in
non-reasoning tasks. To address these challenges, we introduce the
Heuristic-Driven Link-of-Analogy (HD-LoA) prompting method. Specifically, we
hypothesize and validate that LLMs learn task-specific heuristics from
demonstrations via ICL. Building upon this hypothesis, we introduce an explicit
heuristic-driven demonstration construction approach, which transforms the
haphazard example selection process into a methodical method that emphasizes
task heuristics. Additionally, inspired by the analogical reasoning of human,
we propose the link-of-analogy prompting, which enables LLMs to process new
situations by drawing analogies to known situations, enhancing their
adaptability. Extensive experiments show that our method outperforms the
existing prompting methods and few-shot supervised learning methods, exhibiting
F1 score improvements of 4.53% and 9.38% on the document-level EAE dataset.
Furthermore, when applied to sentiment analysis and natural language inference
tasks, the HD-LoA prompting achieves accuracy gains of 2.87% and 2.63%,
indicating its effectiveness across different tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Combining Past, Present and Future: A Self-Supervised Approach for Class Incremental Learning
Abstract: Class Incremental Learning (CIL) aims to handle the scenario where data of
novel classes occur continuously and sequentially. The model should recognize
the sequential novel classes while alleviating the catastrophic forgetting. In
the self-supervised manner, it becomes more challenging to avoid the conflict
between the feature embedding spaces of novel classes and old ones without any
class labels. To address the problem, we propose a self-supervised CIL
framework CPPF, meaning Combining Past, Present and Future. In detail, CPPF
consists of a prototype clustering module (PC), an embedding space reserving
module (ESR) and a multi-teacher distillation module (MTD). 1) The PC and the
ESR modules reserve embedding space for subsequent phases at the prototype
level and the feature level respectively to prepare for knowledge learned in
the future. 2) The MTD module maintains the representations of the current
phase without the interference of past knowledge. One of the teacher networks
retains the representations of the past phases, and the other teacher network
distills relation information of the current phase to the student network.
Extensive experiments on CIFAR100 and ImageNet100 datasets demonstrate that our
proposed method boosts the performance of self-supervised class incremental
learning. We will release code in the near future.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: The voraus-AD Dataset for Anomaly Detection in Robot Applications
Abstract: During the operation of industrial robots, unusual events may endanger the
safety of humans and the quality of production. When collecting data to detect
such cases, it is not ensured that data from all potentially occurring errors
is included as unforeseeable events may happen over time. Therefore, anomaly
detection (AD) delivers a practical solution, using only normal data to learn
to detect unusual events. We introduce a dataset that allows training and
benchmarking of anomaly detection methods for robotic applications based on
machine data which will be made publicly available to the research community.
As a typical robot task the dataset includes a pick-and-place application which
involves movement, actions of the end effector and interactions with the
objects of the environment. Since several of the contained anomalies are not
task-specific but general, evaluations on our dataset are transferable to other
robotics applications as well. Additionally, we present MVT-Flow (multivariate
time-series flow) as a new baseline method for anomaly detection: It relies on
deep-learning-based density estimation with normalizing flows, tailored to the
data domain by taking its structure into account for the architecture. Our
evaluation shows that MVT-Flow outperforms baselines from previous work by a
large margin of 6.2% in area under ROC.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: StyleCrafter: Enhancing Stylized Text-to-Video Generation with Style Adapter
Abstract: Text-to-video (T2V) models have shown remarkable capabilities in generating
diverse videos. However, they struggle to produce user-desired stylized videos
due to (i) text's inherent clumsiness in expressing specific styles and (ii)
the generally degraded style fidelity. To address these challenges, we
introduce StyleCrafter, a generic method that enhances pre-trained T2V models
with a style control adapter, enabling video generation in any style by
providing a reference image. Considering the scarcity of stylized video
datasets, we propose to first train a style control adapter using style-rich
image datasets, then transfer the learned stylization ability to video
generation through a tailor-made finetuning paradigm. To promote content-style
disentanglement, we remove style descriptions from the text prompt and extract
style information solely from the reference image using a decoupling learning
strategy. Additionally, we design a scale-adaptive fusion module to balance the
influences of text-based content features and image-based style features, which
helps generalization across various text and style combinations. StyleCrafter
efficiently generates high-quality stylized videos that align with the content
of the texts and resemble the style of the reference images. Experiments
demonstrate that our approach is more flexible and efficient than existing
competitors.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models
Abstract: The rapid digitization of real-world data offers an unprecedented opportunity
for optimizing healthcare delivery and accelerating biomedical discovery. In
practice, however, such data is most abundantly available in unstructured
forms, such as clinical notes in electronic medical records (EMRs), and it is
generally plagued by confounders. In this paper, we present TRIALSCOPE, a
unifying framework for distilling real-world evidence from population-level
observational data. TRIALSCOPE leverages biomedical language models to
structure clinical text at scale, employs advanced probabilistic modeling for
denoising and imputation, and incorporates state-of-the-art causal inference
techniques to combat common confounders. Using clinical trial specification as
generic representation, TRIALSCOPE provides a turn-key solution to generate and
reason with clinical hypotheses using observational data. In extensive
experiments and analyses on a large-scale real-world dataset with over one
million cancer patients from a large US healthcare network, we show that
TRIALSCOPE can produce high-quality structuring of real-world data and
generates comparable results to marquee cancer trials. In addition to
facilitating in-silicon clinical trial design and optimization, TRIALSCOPE may
be used to empower synthetic controls, pragmatic trials, post-market
surveillance, as well as support fine-grained patient-like-me reasoning in
precision diagnosis and treatment.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition
Abstract: With the advent of deep learning, progressively larger neural networks have
been designed to solve complex tasks. We take advantage of these capacity-rich
models to lower the cost of inference by exploiting computation in
superposition. To reduce the computational burden per input, we propose
Multiple-Input-Multiple-Output Neural Networks (MIMONets) capable of handling
many inputs at once. MIMONets augment various deep neural network architectures
with variable binding mechanisms to represent an arbitrary number of inputs in
a compositional data structure via fixed-width distributed representations.
Accordingly, MIMONets adapt nonlinear neural transformations to process the
data structure holistically, leading to a speedup nearly proportional to the
number of superposed input items in the data structure. After processing in
superposition, an unbinding mechanism recovers each transformed input of
interest. MIMONets also provide a dynamic trade-off between accuracy and
throughput by an instantaneous on-demand switching between a set of
accuracy-throughput operating points, yet within a single set of fixed
parameters. We apply the concept of MIMONets to both CNN and Transformer
architectures resulting in MIMOConv and MIMOFormer, respectively. Empirical
evaluations show that MIMOConv achieves about 2-4 x speedup at an accuracy
delta within [+0.68, -3.18]% compared to WideResNet CNNs on CIFAR10 and
CIFAR100. Similarly, MIMOFormer can handle 2-4 inputs at once while maintaining
a high average accuracy within a [-1.07, -3.43]% delta on the long range arena
benchmark. Finally, we provide mathematical bounds on the interference between
superposition channels in MIMOFormer. Our code is available at
https://github.com/IBM/multiple-input-multiple-output-nets.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Thermal Face Image Classification using Deep Learning Techniques
Abstract: Thermal images have various applications in security, medical and industrial
domains. This paper proposes a practical deep-learning approach for thermal
image classification. Accurate and efficient classification of thermal images
poses a significant challenge across various fields due to the complex image
content and the scarcity of annotated datasets. This work uses a convolutional
neural network (CNN) architecture, specifically ResNet-50 and VGGNet-19, to
extract features from thermal images. This work also applied Kalman filter on
thermal input images for image denoising. The experimental results demonstrate
the effectiveness of the proposed approach in terms of accuracy and efficiency.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Integration and Implementation Strategies for AI Algorithm Deployment with Smart Routing Rules and Workflow Management
Abstract: This paper reviews the challenges hindering the widespread adoption of
artificial intelligence (AI) solutions in the healthcare industry, focusing on
computer vision applications for medical imaging, and how interoperability and
enterprise-grade scalability can be used to address these challenges. The
complex nature of healthcare workflows, intricacies in managing large and
secure medical imaging data, and the absence of standardized frameworks for AI
development pose significant barriers and require a new paradigm to address
them.
  The role of interoperability is examined in this paper as a crucial factor in
connecting disparate applications within healthcare workflows. Standards such
as DICOM, Health Level 7 (HL7), and Integrating the Healthcare Enterprise (IHE)
are highlighted as foundational for common imaging workflows. A specific focus
is placed on the role of DICOM gateways, with Smart Routing Rules and Workflow
Management leading transformational efforts in this area.
  To drive enterprise scalability, new tools are needed. Project MONAI,
established in 2019, is introduced as an initiative aiming to redefine the
development of medical AI applications. The MONAI Deploy App SDK, a component
of Project MONAI, is identified as a key tool in simplifying the packaging and
deployment process, enabling repeatable, scalable, and standardized deployment
patterns for AI applications.
  The abstract underscores the potential impact of successful AI adoption in
healthcare, offering physicians both life-saving and time-saving insights and
driving efficiencies in radiology department workflows. The collaborative
efforts between academia and industry, are emphasized as essential for
advancing the adoption of healthcare AI solutions.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Extracting Self-Consistent Causal Insights from Users Feedback with LLMs and In-context Learning
Abstract: Microsoft Windows Feedback Hub is designed to receive customer feedback on a
wide variety of subjects including critical topics such as power and battery.
Feedback is one of the most effective ways to have a grasp of users' experience
with Windows and its ecosystem. However, the sheer volume of feedback received
by Feedback Hub makes it immensely challenging to diagnose the actual cause of
reported issues. To better understand and triage issues, we leverage Double
Machine Learning (DML) to associate users' feedback with telemetry signals. One
of the main challenges we face in the DML pipeline is the necessity of domain
knowledge for model design (e.g., causal graph), which sometimes is either not
available or hard to obtain. In this work, we take advantage of reasoning
capabilities in Large Language Models (LLMs) to generate a prior model that
which to some extent compensates for the lack of domain knowledge and could be
used as a heuristic for measuring feedback informativeness. Our LLM-based
approach is able to extract previously known issues, uncover new bugs, and
identify sequences of events that lead to a bug, while minimizing out-of-domain
outputs.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: The Development of LLMs for Embodied Navigation
Abstract: In recent years, the rapid advancement of Large Language Models (LLMs) such
as the Generative Pre-trained Transformer (GPT) has attracted increasing
attention due to their potential in a variety of practical applications. The
application of LLMs with Embodied Intelligence has emerged as a significant
area of focus. Among the myriad applications of LLMs, navigation tasks are
particularly noteworthy because they demand a deep understanding of the
environment and quick, accurate decision-making. LLMs can augment embodied
intelligence systems with sophisticated environmental perception and
decision-making support, leveraging their robust language and image-processing
capabilities. This article offers an exhaustive summary of the symbiosis
between LLMs and embodied intelligence with a focus on navigation. It reviews
state-of-the-art models, research methodologies, and assesses the advantages
and disadvantages of existing embodied navigation models and datasets. Finally,
the article elucidates the role of LLMs in embodied intelligence, based on
current research, and forecasts future directions in the field. A comprehensive
list of studies in this survey is available at
https://github.com/Rongtao-Xu/Awesome-LLM-EN

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge
Abstract: LLMs and AI chatbots have improved people's efficiency in various fields.
However, the necessary knowledge for answering the question may be beyond the
models' knowledge boundaries. To mitigate this issue, many researchers try to
introduce external knowledge, such as knowledge graphs and Internet contents,
into LLMs for up-to-date information. However, the external information from
the Internet may include counterfactual information that will confuse the model
and lead to an incorrect response. Thus there is a pressing need for LLMs to
possess the ability to distinguish reliable information from external
knowledge. Therefore, to evaluate the ability of LLMs to discern the
reliability of external knowledge, we create a benchmark from existing
knowledge bases. Our benchmark consists of two tasks, Question Answering and
Text Generation, and for each task, we provide models with a context containing
counterfactual information. Evaluation results show that existing LLMs are
susceptible to interference from unreliable external knowledge with
counterfactual information, and simple intervention methods make limited
contributions to the alleviation of this issue.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: EduGym: An Environment Suite for Reinforcement Learning Education
Abstract: Due to the empirical success of reinforcement learning, an increasing number
of students study the subject. However, from our practical teaching experience,
we see students entering the field (bachelor, master and early PhD) often
struggle. On the one hand, textbooks and (online) lectures provide the
fundamentals, but students find it hard to translate between equations and
code. On the other hand, public codebases do provide practical examples, but
the implemented algorithms tend to be complex, and the underlying test
environments contain multiple reinforcement learning challenges at once.
Although this is realistic from a research perspective, it often hinders
educational conceptual understanding. To solve this issue we introduce EduGym,
a set of educational reinforcement learning environments and associated
interactive notebooks tailored for education. Each EduGym environment is
specifically designed to illustrate a certain aspect/challenge of reinforcement
learning (e.g., exploration, partial observability, stochasticity, etc.), while
the associated interactive notebook explains the challenge and its possible
solution approaches, connecting equations and code in a single document. An
evaluation among RL students and researchers shows 86% of them think EduGym is
a useful tool for reinforcement learning education. All notebooks are available
from https://sites.google.com/view/edu-gym/home, while the full software
package can be installed from https://github.com/RLG-Leiden/edugym.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: The Ethics of Automating Legal Actors
Abstract: The introduction of large public legal datasets has brought about a
renaissance in legal NLP. Many of these datasets are comprised of legal
judgements - the product of judges deciding cases. This fact, together with the
way machine learning works, means that several legal NLP models are models of
judges. While some have argued for the automation of judges, in this position
piece, we argue that automating the role of the judge raises difficult ethical
challenges, in particular for common law legal systems. Our argument follows
from the social role of the judge in actively shaping the law, rather than
merely applying it. Since current NLP models come nowhere close to having the
facilities necessary for this task, they should not be used to automate judges.
Furthermore, even in the case the models could achieve human-level
capabilities, there would still be remaining ethical concerns inherent in the
automation of the legal process.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars
Abstract: Can we avoid wars at the crossroads of history? This question has been
pursued by individuals, scholars, policymakers, and organizations throughout
human history. In this research, we attempt to answer the question based on the
recent advances of Artificial Intelligence (AI) and Large Language Models
(LLMs). We propose \textbf{WarAgent}, an LLM-powered multi-agent AI system, to
simulate the participating countries, their decisions, and the consequences, in
historical international conflicts, including the World War I (WWI), the World
War II (WWII), and the Warring States Period (WSP) in Ancient China. By
evaluating the simulation effectiveness, we examine the advancements and
limitations of cutting-edge AI systems' abilities in studying complex
collective human behaviors such as international conflicts under diverse
settings. In these simulations, the emergent interactions among agents also
offer a novel perspective for examining the triggers and conditions that lead
to war. Our findings offer data-driven and AI-augmented insights that can
redefine how we approach conflict resolution and peacekeeping strategies. The
implications stretch beyond historical analysis, offering a blueprint for using
AI to understand human history and possibly prevent future international
conflicts. Code and data are available at
\url{https://github.com/agiresearch/WarAgent}.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Sleep Deprivation in the Forward-Forward Algorithm
Abstract: This paper aims to explore the separation of the two forward passes in the
Forward-Forward algorithm from a biological perspective in the context of
sleep. We show the size of the gap between the sleep and awake phase influences
the learning capabilities of the algorithm and highlight the importance of
negative data in diminishing the devastating effects of sleep deprivation.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Sequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generation
Abstract: Model hallucination has been a crucial interest of research in Natural
Language Generation (NLG). In this work, we propose sequence-level certainty as
a common theme over hallucination in NLG, and explore the correlation between
sequence-level certainty and the level of hallucination in model responses. We
categorize sequence-level certainty into two aspects: probabilistic certainty
and semantic certainty, and reveal through experiments on Knowledge-Grounded
Dialogue Generation (KGDG) task that both a higher level of probabilistic
certainty and a higher level of semantic certainty in model responses are
significantly correlated with a lower level of hallucination. What's more, we
provide theoretical proof and analysis to show that semantic certainty is a
good estimator of probabilistic certainty, and therefore has the potential as
an alternative to probability-based certainty estimation in black-box
scenarios. Based on the observation on the relationship between certainty and
hallucination, we further propose Certainty-based Response Ranking (CRR), a
decoding-time method for mitigating hallucination in NLG. Based on our
categorization of sequence-level certainty, we propose 2 types of CRR approach:
Probabilistic CRR (P-CRR) and Semantic CRR (S-CRR). P-CRR ranks individually
sampled model responses using their arithmetic mean log-probability of the
entire sequence. S-CRR approaches certainty estimation from meaning-space, and
ranks a number of model response candidates based on their semantic certainty
level, which is estimated by the entailment-based Agreement Score (AS). Through
extensive experiments across 3 KGDG datasets, 3 decoding methods, and on 4
different models, we validate the effectiveness of our 2 proposed CRR methods
to reduce model hallucination.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Towards Explainable Strategy Templates using NLP Transformers
Abstract: This paper bridges the gap between mathematical heuristic strategies learned
from Deep Reinforcement Learning (DRL) in automated agent negotiation, and
comprehensible, natural language explanations. Our aim is to make these
strategies more accessible to non-experts. By leveraging traditional Natural
Language Processing (NLP) techniques and Large Language Models (LLMs) equipped
with Transformers, we outline how parts of DRL strategies composed of parts
within strategy templates can be transformed into user-friendly, human-like
English narratives. To achieve this, we present a top-level algorithm that
involves parsing mathematical expressions of strategy templates, semantically
interpreting variables and structures, generating rule-based primary
explanations, and utilizing a Generative Pre-trained Transformer (GPT) model to
refine and contextualize these explanations. Subsequent customization for
varied audiences and meticulous validation processes in an example illustrate
the applicability and potential of this approach.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models
Abstract: Large Language Models (LLMs) with a billion or more parameters are prime
targets for network pruning, which aims to reduce a portion of the network
weights without compromising performance. Prior approaches such as Weights
Magnitude, SparseGPT, and Wanda, either concentrated solely on weights or
integrated weights with activations for sparsity. However, they overlooked the
informative gradients derived from pretrained large language models. In this
paper, we present a novel sparsity-centric pruning method for pretrained LLMs,
termed Gradient-based Language Model Pruner (GBLM-Pruner). GBLM-Pruner
leverages the first-order term of the Taylor expansion, operating in a
training-free manner by harnessing properly normalized gradients from a few
calibration samples to determine the importance pruning score, and
substantially outperforms competitive counterparts like SparseGPT and Wanda in
multiple benchmarks. Intriguing, after incorporating gradients, the
unstructured pruning method tends to reveal some structural patterns
post-pruning, which mirrors the geometric interdependence inherent in the LLMs'
parameter structure. Additionally, GBLM-Pruner functions without any subsequent
retraining or weight updates to maintain its simplicity as other counterparts.
Extensive evaluations on LLaMA-1 and LLaMA-2 across various language benchmarks
and perplexity show that GBLM-Pruner surpasses magnitude pruning, Wanda
(weights+activations) and SparseGPT (weights+activations+weight update) by
significant margins. Our code and models are available at
https://github.com/RocktimJyotiDas/GBLM-Pruner.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression
Abstract: The lack of explainability using relevant clinical knowledge hinders the
adoption of Artificial Intelligence-powered analysis of unstructured clinical
dialogue. A wealth of relevant, untapped Mental Health (MH) data is available
in online communities, providing the opportunity to address the explainability
problem with substantial potential impact as a screening tool for both online
and offline applications. We develop a method to enhance attention in popular
transformer models and generate clinician-understandable explanations for
classification by incorporating external clinical knowledge. Inspired by how
clinicians rely on their expertise when interacting with patients, we leverage
relevant clinical knowledge to model patient inputs, providing meaningful
explanations for classification. This will save manual review time and engender
trust. We develop such a system in the context of MH using clinical practice
guidelines (CPG) for diagnosing depression, a mental health disorder of global
concern. We propose an application-specific language model called ProcesS
knowledge-infused cross ATtention (PSAT), which incorporates CPGs when
computing attention. Through rigorous evaluation on three expert-curated
datasets related to depression, we demonstrate application-relevant
explainability of PSAT. PSAT also surpasses the performance of nine baseline
models and can provide explanations where other baselines fall short. We
transform a CPG resource focused on depression, such as the Patient Health
Questionnaire (e.g. PHQ-9) and related questions, into a machine-readable
ontology using SNOMED-CT. With this resource, PSAT enhances the ability of
models like GPT-3.5 to generate application-relevant explanations.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark
Abstract: Unsupervised Domain Adaptation (UDA) aims to harness labeled source data to
train models for unlabeled target data. Despite extensive research in domains
like computer vision and natural language processing, UDA remains underexplored
for time series data, which has widespread real-world applications ranging from
medicine and manufacturing to earth observation and human activity recognition.
Our paper addresses this gap by introducing a comprehensive benchmark for
evaluating UDA techniques for time series classification, with a focus on deep
learning methods. We provide seven new benchmark datasets covering various
domain shifts and temporal dynamics, facilitating fair and standardized UDA
method assessments with state of the art neural network backbones (e.g.
Inception) for time series data. This benchmark offers insights into the
strengths and limitations of the evaluated approaches while preserving the
unsupervised nature of domain adaptation, making it directly applicable to
practical problems. Our paper serves as a vital resource for researchers and
practitioners, advancing domain adaptation solutions for time series data and
fostering innovation in this critical field. The implementation code of this
benchmark is available at https://github.com/EricssonResearch/UDA-4-TSC.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: DocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding
Abstract: This work presents DocPedia, a novel large multimodal model (LMM) for
versatile OCR-free document understanding, capable of parsing images up to
2,560$\times$2,560 resolution. Unlike existing work either struggle with
high-resolution documents or give up the large language model thus vision or
language ability constrained, our DocPedia directly processes visual input in
the frequency domain rather than the pixel space. The unique characteristic
enables DocPedia to capture a greater amount of visual and textual information
using a limited number of visual tokens. To consistently enhance both
perception and comprehension abilities of our model, we develop a dual-stage
training strategy and enrich instructions/annotations of all training tasks
covering multiple document types. Extensive quantitative and qualitative
experiments conducted on various publicly available benchmarks confirm the
mutual benefits of jointly learning perception and comprehension tasks. The
results provide further evidence of the effectiveness and superior performance
of our DocPedia over other methods.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects
Abstract: Policy learning in robot-assisted surgery (RAS) lacks data efficient and
versatile methods that exhibit the desired motion quality for delicate surgical
interventions. To this end, we introduce Movement Primitive Diffusion (MPD), a
novel method for imitation learning (IL) in RAS that focuses on gentle
manipulation of deformable objects. The approach combines the versatility of
diffusion-based imitation learning (DIL) with the high-quality motion
generation capabilities of Probabilistic Dynamic Movement Primitives (ProDMPs).
This combination enables MPD to achieve gentle manipulation of deformable
objects, while maintaining data efficiency critical for RAS applications where
demonstration data is scarce. We evaluate MPD across various simulated tasks
and a real world robotic setup on both state and image observations. MPD
outperforms state-of-the-art DIL methods in success rate, motion quality, and
data efficiency.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Comparing Generative Chatbots Based on Process Requirements
Abstract: Business processes are commonly represented by modelling languages, such as
Event-driven Process Chain (EPC), Yet Another Workflow Language (YAWL), and the
most popular standard notation for modelling business processes, the Business
Process Model and Notation (BPMN). Most recently, chatbots, programs that allow
users to interact with a machine using natural language, have been increasingly
used for business process execution support. A recent category of chatbots
worth mentioning is generative-based chatbots, powered by Large Language Models
(LLMs) such as OpenAI's Generative Pre-Trained Transformer (GPT) model and
Google's Pathways Language Model (PaLM), which are trained on billions of
parameters and support conversational intelligence. However, it is not clear
whether generative-based chatbots are able to understand and meet the
requirements of constructs such as those provided by BPMN for process execution
support. This paper presents a case study to compare the performance of
prominent generative models, GPT and PaLM, in the context of process execution
support. The research sheds light into the challenging problem of using
conversational approaches supported by generative chatbots as a means to
understand process-aware modelling notations and support users to execute their
tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: SurreyAI 2023 Submission for the Quality Estimation Shared Task
Abstract: Quality Estimation (QE) systems are important in situations where it is
necessary to assess the quality of translations, but there is no reference
available. This paper describes the approach adopted by the SurreyAI team for
addressing the Sentence-Level Direct Assessment shared task in WMT23. The
proposed approach builds upon the TransQuest framework, exploring various
autoencoder pre-trained language models within the MonoTransQuest architecture
using single and ensemble settings. The autoencoder pre-trained language models
employed in the proposed systems are XLMV, InfoXLM-large, and XLMR-large. The
evaluation utilizes Spearman and Pearson correlation coefficients, assessing
the relationship between machine-predicted quality scores and human judgments
for 5 language pairs (English-Gujarati, English-Hindi, English-Marathi,
English-Tamil and English-Telugu). The MonoTQ-InfoXLM-large approach emerges as
a robust strategy, surpassing all other individual models proposed in this
study by significantly improving over the baseline for the majority of the
language pairs.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: AI Competitions and Benchmarks: towards impactful challenges with post-challenge papers, benchmarks and other dissemination actions
Abstract: Organising an AI challenge does not end with the final event. The
long-lasting impact also needs to be organised. This chapter covers the various
activities after the challenge is formally finished. The target audience of
different post-challenge activities is identified. The various outputs of the
challenge are listed with the means to collect them. The main part of the
chapter is a template for a typical post-challenge paper, including possible
graphs as well as advice on how to turn the challenge into a long-lasting
benchmark.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Are we going MAD? Benchmarking Multi-Agent Debate between Language Models for Medical Q&A
Abstract: Recent advancements in large language models (LLMs) underscore their
potential for responding to medical inquiries. However, ensuring that
generative agents provide accurate and reliable answers remains an ongoing
challenge. In this context, multi-agent debate (MAD) has emerged as a prominent
strategy for enhancing the truthfulness of LLMs. In this work, we provide a
comprehensive benchmark of MAD strategies for medical Q&A, along with
open-source implementations. This explores the effective utilization of various
strategies including the trade-offs between cost, time, and accuracy. We build
upon these insights to provide a novel debate-prompting strategy based on agent
agreement that outperforms previously published strategies on medical Q&A
tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: A Bag of Receptive Fields for Time Series Extrinsic Predictions
Abstract: High-dimensional time series data poses challenges due to its dynamic nature,
varying lengths, and presence of missing values. This kind of data requires
extensive preprocessing, limiting the applicability of existing Time Series
Classification and Time Series Extrinsic Regression techniques. For this
reason, we propose BORF, a Bag-Of-Receptive-Fields model, which incorporates
notions from time series convolution and 1D-SAX to handle univariate and
multivariate time series with varying lengths and missing values. We evaluate
BORF on Time Series Classification and Time Series Extrinsic Regression tasks
using the full UEA and UCR repositories, demonstrating its competitive
performance against state-of-the-art methods. Finally, we outline how this
representation can naturally provide saliency and feature-based explanations.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Unmasking Bias and Inequities: A Systematic Review of Bias Detection and Mitigation in Healthcare Artificial Intelligence Using Electronic Health Records
Abstract: Objectives: Artificial intelligence (AI) applications utilizing electronic
health records (EHRs) have gained popularity, but they also introduce various
types of bias. This study aims to systematically review the literature that
address bias in AI research utilizing EHR data. Methods: A systematic review
was conducted following the Preferred Reporting Items for Systematic Reviews
and Meta-analyses (PRISMA) guideline. We retrieved articles published between
January 1, 2010, and October 31, 2022, from PubMed, Web of Science, and the
Institute of Electrical and Electronics Engineers. We defined six major types
of bias and summarized the existing approaches in bias handling. Results: Out
of the 252 retrieved articles, 20 met the inclusion criteria for the final
review. Five out of six bias were covered in this review: eight studies
analyzed selection bias; six on implicit bias; five on confounding bias; four
on measurement bias; two on algorithmic bias. For bias handling approaches, ten
studies identified bias during model development, while seventeen presented
methods to mitigate the bias. Discussion: Bias may infiltrate the AI
application development process at various stages. Although this review
discusses methods for addressing bias at different development stages, there is
room for implementing additional effective approaches. Conclusion: Despite
growing attention to bias in healthcare AI, research using EHR data on this
topic is still limited. Detecting and mitigating AI bias with EHR data
continues to pose challenges. Further research is needed to raise a
standardized method that is generalizable and interpretable to detect, mitigate
and evaluate bias in medical AI.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Weakly Supervised Semantic Parsing with Execution-based Spurious Program Filtering
Abstract: The problem of spurious programs is a longstanding challenge when training a
semantic parser from weak supervision. To eliminate such programs that have
wrong semantics but correct denotation, existing methods focus on exploiting
similarities between examples based on domain-specific knowledge. In this
paper, we propose a domain-agnostic filtering mechanism based on program
execution results. Specifically, for each program obtained through the search
process, we first construct a representation that captures the program's
semantics as execution results under various inputs. Then, we run a majority
vote on these representations to identify and filter out programs with
significantly different semantics from the other programs. In particular, our
method is orthogonal to the program search process so that it can easily
augment any of the existing weakly supervised semantic parsing frameworks.
Empirical evaluations on the Natural Language Visual Reasoning and
WikiTableQuestions demonstrate that applying our method to the existing
semantic parsers induces significantly improved performances.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoML
Abstract: AutoML platforms have numerous options for the algorithms to try for each
step of the analysis, i.e., different possible algorithms for imputation,
transformations, feature selection, and modelling. Finding the optimal
combination of algorithms and hyper-parameter values is computationally
expensive, as the number of combinations to explore leads to an exponential
explosion of the space. In this paper, we present the Sequential
Hyper-parameter Space Reduction (SHSR) algorithm that reduces the space for an
AutoML tool with negligible drop in its predictive performance. SHSR is a
meta-level learning algorithm that analyzes past runs of an AutoML tool on
several datasets and learns which hyper-parameter values to filter out from
consideration on a new dataset to analyze. SHSR is evaluated on 284
classification and 375 regression problems, showing an approximate 30%
reduction in execution time with a performance drop of less than 0.1%.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: OASIS: Offsetting Active Reconstruction Attacks in Federated Learning
Abstract: Federated Learning (FL) has garnered significant attention for its potential
to protect user privacy while enhancing model training efficiency. However,
recent research has demonstrated that FL protocols can be easily compromised by
active reconstruction attacks executed by dishonest servers. These attacks
involve the malicious modification of global model parameters, allowing the
server to obtain a verbatim copy of users' private data by inverting their
gradient updates. Tackling this class of attack remains a crucial challenge due
to the strong threat model. In this paper, we propose OASIS, a defense
mechanism based on image augmentation that effectively counteracts active
reconstruction attacks while preserving model performance. We first uncover the
core principle of gradient inversion that enables these attacks and
theoretically identify the main conditions by which the defense can be robust
regardless of the attack strategies. We then construct OASIS with image
augmentation showing that it can undermine the attack principle. Comprehensive
evaluations demonstrate the efficacy of OASIS highlighting its feasibility as a
solution.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Difference of Probability and Information Entropy for Skills Classification and Prediction in Student Learning
Abstract: The probability of an event is in the range of [0, 1]. In a sample space S,
the value of probability determines whether an outcome is true or false. The
probability of an event Pr(A) that will never occur = 0. The probability of the
event Pr(B) that will certainly occur = 1. This makes both events A and B thus
a certainty. Furthermore, the sum of probabilities Pr(E1) + Pr(E2) + ... +
Pr(En) of a finite set of events in a given sample space S = 1. Conversely, the
difference of the sum of two probabilities that will certainly occur is 0.
Firstly, this paper discusses Bayes' theorem, then complement of probability
and the difference of probability for occurrences of learning-events, before
applying these in the prediction of learning objects in student learning. Given
the sum total of 1; to make recommendation for student learning, this paper
submits that the difference of argMaxPr(S) and probability of
student-performance quantifies the weight of learning objects for students.
Using a dataset of skill-set, the computational procedure demonstrates: i) the
probability of skill-set events that has occurred that would lead to higher
level learning; ii) the probability of the events that has not occurred that
requires subject-matter relearning; iii) accuracy of decision tree in the
prediction of student performance into class labels; and iv) information
entropy about skill-set data and its implication on student cognitive
performance and recommendation of learning [1].

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: A novel transformer-based approach for soil temperature prediction
Abstract: Soil temperature is one of the most significant parameters that plays a
crucial role in glacier energy, dynamics of mass balance, processes of surface
hydrological, coaction of glacier-atmosphere, nutrient cycling, ecological
stability, the management of soil, water, and field crop. In this work, we
introduce a novel approach using transformer models for the purpose of
forecasting soil temperature prediction. To the best of our knowledge, the
usage of transformer models in this work is the very first attempt to predict
soil temperature. Experiments are carried out using six different FLUXNET
stations by modeling them with five different transformer models, namely,
Vanilla Transformer, Informer, Autoformer, Reformer, and ETSformer. To
demonstrate the effectiveness of the proposed model, experiment results are
compared with both deep learning approaches and literature studies. Experiment
results show that the utilization of transformer models ensures a significant
contribution to the literature, thence determining the new state-of-the-art.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction
Abstract: Extracting users' interests from their lifelong behavior sequence is crucial
for predicting Click-Through Rate (CTR). Most current methods employ a
two-stage process for efficiency: they first select historical behaviors
related to the candidate item and then deduce the user's interest from this
narrowed-down behavior sub-sequence. This two-stage paradigm, though effective,
leads to information loss. Solely using users' lifelong click behaviors doesn't
provide a complete picture of their interests, leading to suboptimal
performance. In our research, we introduce the Deep Group Interest Network
(DGIN), an end-to-end method to model the user's entire behavior history. This
includes all post-registration actions, such as clicks, cart additions,
purchases, and more, providing a nuanced user understanding. We start by
grouping the full range of behaviors using a relevant key (like item_id) to
enhance efficiency. This process reduces the behavior length significantly,
from O(10^4) to O(10^2). To mitigate the potential loss of information due to
grouping, we incorporate two categories of group attributes. Within each group,
we calculate statistical information on various heterogeneous behaviors (like
behavior counts) and employ self-attention mechanisms to highlight unique
behavior characteristics (like behavior type). Based on this reorganized
behavior data, the user's interests are derived using the Transformer
technique. Additionally, we identify a subset of behaviors that share the same
item_id with the candidate item from the lifelong behavior sequence. The
insights from this subset reveal the user's decision-making process related to
the candidate item, improving prediction accuracy. Our comprehensive
evaluation, both on industrial and public datasets, validates DGIN's efficacy
and efficiency.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: chatGPT for generating questions and assessments based on accreditations
Abstract: This research aims to take advantage of artificial intelligence techniques in
producing students assessment that is compatible with the different academic
accreditations of the same program. The possibility of using generative
artificial intelligence technology was studied to produce an academic
accreditation compliant test the National Center for Academic Accreditation of
Kingdom of Saudi Arabia and Accreditation Board for Engineering and Technology.
A novel method was introduced to map the verbs used to create the questions
introduced in the tests. The method allows a possibility of using the
generative artificial intelligence technology to produce and check the validity
of questions that measure educational outcomes. A questionnaire was distributed
to ensure that the use of generative artificial intelligence to create exam
questions is acceptable by the faculty members, as well as to ask about the
acceptance of assistance in validating questions submitted by faculty members
and amending them in accordance with academic accreditations. The questionnaire
was distributed to faculty members of different majors in the Kingdom of Saudi
Arabias universities. one hundred twenty responses obtained with eight five
percentile approval percentage for generate complete exam questions by
generative artificial intelligence . Whereas ninety eight percentage was the
approval percentage for editing and improving already existed questions.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation
Abstract: Transformers have been successfully applied in the field of video-based 3D
human pose estimation. However, the high computational costs of these video
pose transformers (VPTs) make them impractical on resource-constrained devices.
In this paper, we present a plug-and-play pruning-and-recovering framework,
called Hourglass Tokenizer (HoT), for efficient transformer-based 3D human pose
estimation from videos. Our HoT begins with pruning pose tokens of redundant
frames and ends with recovering full-length tokens, resulting in a few pose
tokens in the intermediate transformer blocks and thus improving the model
efficiency. To effectively achieve this, we propose a token pruning cluster
(TPC) that dynamically selects a few representative tokens with high semantic
diversity while eliminating the redundancy of video frames. In addition, we
develop a token recovering attention (TRA) to restore the detailed
spatio-temporal information based on the selected tokens, thereby expanding the
network output to the original full-length temporal resolution for fast
inference. Extensive experiments on two benchmark datasets (i.e., Human3.6M and
MPI-INF-3DHP) demonstrate that our method can achieve both high efficiency and
estimation accuracy compared to the original VPT models. For instance, applying
to MotionBERT and MixSTE on Human3.6M, our HoT can save nearly 50% FLOPs
without sacrificing accuracy and nearly 40% FLOPs with only 0.2% accuracy drop,
respectively. Our source code will be open-sourced.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: The Disagreement Problem in Faithfulness Metrics
Abstract: The field of explainable artificial intelligence (XAI) aims to explain how
black-box machine learning models work. Much of the work centers around the
holy grail of providing post-hoc feature attributions to any model
architecture. While the pace of innovation around novel methods has slowed
down, the question remains of how to choose a method, and how to make it fit
for purpose. Recently, efforts around benchmarking XAI methods have suggested
metrics for that purpose -- but there are many choices. That bounty of choice
still leaves an end user unclear on how to proceed. This paper focuses on
comparing metrics with the aim of measuring faithfulness of local explanations
on tabular classification problems -- and shows that the current metrics don't
agree; leaving users unsure how to choose the most faithful explanations.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Is Feedback All You Need? Leveraging Natural Language Feedback in Goal-Conditioned Reinforcement Learning
Abstract: Despite numerous successes, the field of reinforcement learning (RL) remains
far from matching the impressive generalisation power of human behaviour
learning. One possible way to help bridge this gap be to provide RL agents with
richer, more human-like feedback expressed in natural language. To investigate
this idea, we first extend BabyAI to automatically generate language feedback
from the environment dynamics and goal condition success. Then, we modify the
Decision Transformer architecture to take advantage of this additional signal.
We find that training with language feedback either in place of or in addition
to the return-to-go or goal descriptions improves agents' generalisation
performance, and that agents can benefit from feedback even when this is only
available during training, but not at inference.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: INTERVENOR: Prompt the Coding Ability of Large Language Models with the Interactive Chain of Repairing
Abstract: This paper proposes INTERactiVE chaiN Of Repairing (INTERVENOR), which mimics
human code repairing behavior (iteratively judging, rethinking, and repairing)
and prompts the coding ability of regard Large Language Models (LLMs).
Specifically, INTERVENOR employs two LLM based agents, Code Learner and Code
Teacher, to play different roles in code repairing and work interactively to
repair the generated codes. The Code Learner is asked to generate and repair
code according to the instructions from the Code Teacher. The Code Teacher
rethinks the code errors according to the corresponding feedback from compilers
and iteratively generates the chain-of-repairing (CoR) to guide the code
repairing process for Code Learner. Our experiments show that INTERVENOR
outperforms the state-of-the-art methods and achieves about 13% and 4.5%
improvements over the GPT-3.5 model in code generation and code translation
tasks, respectively. Our further analyses show that CoR can illuminate the bug
reasons and solution plans via natural language. Thanks to the feedback of code
compilers, INTERVENOR can accurately identify the syntax errors and assertion
errors in the code and provide precise instructions to repair codes, making
LLMs achieve the plateau performance with only three repairing turns. All data
and codes are available at https://github.com/NEUIR/INTERVENOR

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks
Abstract: Growing applications of large language models (LLMs) trained by a third party
raise serious concerns on the security vulnerability of LLMs.It has been
demonstrated that malicious actors can covertly exploit these vulnerabilities
in LLMs through poisoning attacks aimed at generating undesirable outputs.
While poisoning attacks have received significant attention in the image domain
(e.g., object detection), and classification tasks, their implications for
generative models, particularly in the realm of natural language generation
(NLG) tasks, remain poorly understood. To bridge this gap, we perform a
comprehensive exploration of various poisoning techniques to assess their
effectiveness across a range of generative tasks. Furthermore, we introduce a
range of metrics designed to quantify the success and stealthiness of poisoning
attacks specifically tailored to NLG tasks. Through extensive experiments on
multiple NLG tasks, LLMs and datasets, we show that it is possible to
successfully poison an LLM during the fine-tuning stage using as little as 1\%
of the total tuning data samples. Our paper presents the first systematic
approach to comprehend poisoning attacks targeting NLG tasks considering a wide
range of triggers and attack settings. We hope our findings will assist the AI
security community in devising appropriate defenses against such threats.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Do Smaller Language Models Answer Contextualised Questions Through Memorisation Or Generalisation?
Abstract: A distinction is often drawn between a model's ability to predict a label for
an evaluation sample that is directly memorised from highly similar training
samples versus an ability to predict the label via some method of
generalisation. In the context of using Language Models for question-answering,
discussion continues to occur as to the extent to which questions are answered
through memorisation. We consider this issue for questions that would ideally
be answered through reasoning over an associated context. We propose a method
of identifying evaluation samples for which it is very unlikely our model would
have memorised the answers. Our method is based on semantic similarity of input
tokens and label tokens between training and evaluation samples. We show that
our method offers advantages upon some prior approaches in that it is able to
surface evaluation-train pairs that have overlap in either contiguous or
discontiguous sequences of tokens. We use this method to identify unmemorisable
subsets of our evaluation datasets. We train two Language Models in a multitask
fashion whereby the second model differs from the first only in that it has two
additional datasets added to the training regime that are designed to impart
simple numerical reasoning strategies of a sort known to improve performance on
some of our evaluation datasets but not on others. We then show that there is
performance improvement between the two models on the unmemorisable subsets of
the evaluation datasets that were expected to benefit from the additional
training datasets. Specifically, performance on unmemorisable subsets of two of
our evaluation datasets, DROP and ROPES significantly improves by 9.0%, and
25.7% respectively while other evaluation datasets have no significant change
in performance.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Advancing Post Hoc Case Based Explanation with Feature Highlighting
Abstract: Explainable AI (XAI) has been proposed as a valuable tool to assist in
downstream tasks involving human and AI collaboration. Perhaps the most
psychologically valid XAI techniques are case based approaches which display
'whole' exemplars to explain the predictions of black box AI systems. However,
for such post hoc XAI methods dealing with images, there has been no attempt to
improve their scope by using multiple clear feature 'parts' of the images to
explain the predictions while linking back to relevant cases in the training
data, thus allowing for more comprehensive explanations that are faithful to
the underlying model. Here, we address this gap by proposing two general
algorithms (latent and super pixel based) which can isolate multiple clear
feature parts in a test image, and then connect them to the explanatory cases
found in the training data, before testing their effectiveness in a carefully
designed user study. Results demonstrate that the proposed approach
appropriately calibrates a users feelings of 'correctness' for ambiguous
classifications in real world data on the ImageNet dataset, an effect which
does not happen when just showing the explanation without feature highlighting.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models
Abstract: The Diffusion model, a prevalent framework for image generation, encounters
significant challenges in terms of broad applicability due to its extended
inference times and substantial memory requirements. Efficient Post-training
Quantization (PTQ) is pivotal for addressing these issues in traditional
models. Different from traditional models, diffusion models heavily depend on
the time-step $t$ to achieve satisfactory multi-round denoising. Usually, $t$
from the finite set $\{1, \ldots, T\}$ is encoded to a temporal feature by a
few modules totally irrespective of the sampling data. However, existing PTQ
methods do not optimize these modules separately. They adopt inappropriate
reconstruction targets and complex calibration methods, resulting in a severe
disturbance of the temporal feature and denoising trajectory, as well as a low
compression efficiency. To solve these, we propose a Temporal Feature
Maintenance Quantization (TFMQ) framework building upon a Temporal Information
Block which is just related to the time-step $t$ and unrelated to the sampling
data. Powered by the pioneering block design, we devise temporal information
aware reconstruction (TIAR) and finite set calibration (FSC) to align the
full-precision temporal features in a limited time. Equipped with the
framework, we can maintain the most temporal information and ensure the
end-to-end generation quality. Extensive experiments on various datasets and
diffusion models prove our state-of-the-art results. Remarkably, our
quantization approach, for the first time, achieves model performance nearly on
par with the full-precision model under 4-bit weight quantization.
Additionally, our method incurs almost no extra computational cost and
accelerates quantization time by $2.0 \times$ on LSUN-Bedrooms $256 \times 256$
compared to previous works.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: ChatGPT in the context of precision agriculture data analytics
Abstract: In this study we argue that integrating ChatGPT into the data processing
pipeline of automated sensors in precision agriculture has the potential to
bring several benefits and enhance various aspects of modern farming practices.
Policy makers often face a barrier when they need to get informed about the
situation in vast agricultural fields to reach to decisions. They depend on the
close collaboration between agricultural experts in the field, data analysts,
and technology providers to create interdisciplinary teams that cannot always
be secured on demand or establish effective communication across these diverse
domains to respond in real-time. In this work we argue that the speech
recognition input modality of ChatGPT provides a more intuitive and natural way
for policy makers to interact with the database of the server of an
agricultural data processing system to which a large, dispersed network of
automated insect traps and sensors probes reports. The large language models
map the speech input to text, allowing the user to form its own version of
unconstrained verbal query, raising the barrier of having to learn and adapt
oneself to a specific data analytics software. The output of the language model
can interact through Python code and Pandas with the entire database, visualize
the results and use speech synthesis to engage the user in an iterative and
refining discussion related to the data. We show three ways of how ChatGPT can
interact with the database of the remote server to which a dispersed network of
different modalities (optical counters, vibration recordings, pictures, and
video), report. We examine the potential and the validity of the response of
ChatGPT in analyzing, and interpreting agricultural data, providing real time
insights and recommendations to stakeholders

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: QualEval: Qualitative Evaluation for Model Improvement
Abstract: Quantitative evaluation metrics have traditionally been pivotal in gauging
the advancements of artificial intelligence systems, including large language
models (LLMs). However, these metrics have inherent limitations. Given the
intricate nature of real-world tasks, a single scalar to quantify and compare
is insufficient to capture the fine-grained nuances of model behavior. Metrics
serve only as a way to compare and benchmark models, and do not yield
actionable diagnostics, thus making the model improvement process challenging.
Model developers find themselves amid extensive manual efforts involving
sifting through vast datasets and attempting hit-or-miss adjustments to
training data or setups. In this work, we address the shortcomings of
quantitative metrics by proposing QualEval, which augments quantitative scalar
metrics with automated qualitative evaluation as a vehicle for model
improvement. QualEval uses a powerful LLM reasoner and our novel flexible
linear programming solver to generate human-readable insights that when
applied, accelerate model improvement. The insights are backed by a
comprehensive dashboard with fine-grained visualizations and
human-interpretable analyses. We corroborate the faithfulness of QualEval by
demonstrating that leveraging its insights, for example, improves the absolute
performance of the Llama 2 model by up to 15% points relative on a challenging
dialogue task (DialogSum) when compared to baselines. QualEval successfully
increases the pace of model development, thus in essence serving as a
data-scientist-in-a-box. Given the focus on critiquing and improving current
evaluation metrics, our method serves as a refreshingly new technique for both
model evaluation and improvement.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: diff History for Long-Context Language Agents
Abstract: Language Models (LMs) offer an exciting solution for general-purpose embodied
control. However, a key technical issue arises when using an LM-based
controller: environment observations must be converted to text, which coupled
with history, leads to prohibitively large textual prompts. As a result, prior
work in LM agents is limited to restricted domains with either small
observation size or minimal needs for interaction history. In this paper, we
introduce a simple and highly effective solution to these issues. We exploit
the fact that consecutive text observations have high similarity and propose to
compress them via the Unix diff command. We demonstrate our approach in
NetHack, a complex rogue-like video game, that requires long-horizon reasoning
for decision-making and is far from solved, particularly for neural agents.
Diff history offers an average of 4x increase in the length of the text-based
interaction history available to the LM. This observational compression along
with the benefits of abstraction yields a 7x improvement in game score on
held-out environment instances over state-of-the-art baselines. It also
outperforms prior agents that use visual observations by over 40%.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Divide-and-Conquer Attack: Harnessing the Power of LLM to Bypass the Censorship of Text-to-Image Generation Model
Abstract: Text-to-image generative models offer many innovative services but also raise
ethical concerns due to their potential to generate unethical images. Most
publicly available text-to-image models employ safety filters to prevent
unintended generation intents. In this work, we introduce the
Divide-and-Conquer Attack to circumvent the safety filters of state-of-the-art
text-to-image models. Our attack leverages LLMs as agents for text
transformation, creating adversarial prompts from sensitive ones. We have
developed effective helper prompts that enable LLMs to break down sensitive
drawing prompts into multiple harmless descriptions, allowing them to bypass
safety filters while still generating sensitive images. This means that the
latent harmful meaning only becomes apparent when all individual elements are
drawn together. Our evaluation demonstrates that our attack successfully
circumvents the closed-box safety filter of SOTA DALLE-3 integrated natively
into ChatGPT to generate unethical images. This approach, which essentially
uses LLM-generated adversarial prompts against GPT-4-assisted DALLE-3, is akin
to using one's own spear to breach their shield. It could have more severe
security implications than previous manual crafting or iterative model querying
methods, and we hope it stimulates more attention towards similar efforts. Our
code and data are available at:
https://github.com/researchcode001/Divide-and-Conquer-Attack

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Dense Video Captioning: A Survey of Techniques, Datasets and Evaluation Protocols
Abstract: Untrimmed videos have interrelated events, dependencies, context, overlapping
events, object-object interactions, domain specificity, and other semantics
that are worth highlighting while describing a video in natural language. Owing
to such a vast diversity, a single sentence can only correctly describe a
portion of the video. Dense Video Captioning (DVC) aims at detecting and
describing different events in a given video. The term DVC originated in the
2017 ActivityNet challenge, after which considerable effort has been made to
address the challenge. Dense Video Captioning is divided into three sub-tasks:
(1) Video Feature Extraction (VFE), (2) Temporal Event Localization (TEL), and
(3) Dense Caption Generation (DCG). This review aims to discuss all the studies
that claim to perform DVC along with its sub-tasks and summarize their results.
We also discuss all the datasets that have been used for DVC. Lastly, we
highlight some emerging challenges and future trends in the field.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Explainable Product Classification for Customs
Abstract: The task of assigning internationally accepted commodity codes (aka HS codes)
to traded goods is a critical function of customs offices. Like court decisions
made by judges, this task follows the doctrine of precedent and can be
nontrivial even for experienced officers. Together with the Korea Customs
Service (KCS), we propose a first-ever explainable decision supporting model
that suggests the most likely subheadings (i.e., the first six digits) of the
HS code. The model also provides reasoning for its suggestion in the form of a
document that is interpretable by customs officers. We evaluated the model
using 5,000 cases that recently received a classification request. The results
showed that the top-3 suggestions made by our model had an accuracy of 93.9\%
when classifying 925 challenging subheadings. A user study with 32 customs
experts further confirmed that our algorithmic suggestions accompanied by
explainable reasonings, can substantially reduce the time and effort taken by
customs officers for classification reviews.

Output: Artificial Intelligence