Instruction: What field is the article from?

Input:
Title: Emotion Recognition by Video: A review
Abstract: Video emotion recognition is an important branch of affective computing, and
its solutions can be applied in different fields such as human-computer
interaction (HCI) and intelligent medical treatment. Although the number of
papers published in the field of emotion recognition is increasing, there are
few comprehensive literature reviews covering related research on video emotion
recognition. Therefore, this paper selects articles published from 2015 to 2023
to systematize the existing trends in video emotion recognition in related
studies. In this paper, we first talk about two typical emotion models, then we
talk about databases that are frequently utilized for video emotion
recognition, including unimodal databases and multimodal databases. Next, we
look at and classify the specific structure and performance of modern unimodal
and multimodal video emotion recognition methods, talk about the benefits and
drawbacks of each, and then we compare them in detail in the tables. Further,
we sum up the primary difficulties right now looked by video emotion
recognition undertakings and point out probably the most encouraging future
headings, such as establishing an open benchmark database and better multimodal
fusion strategys. The essential objective of this paper is to assist scholarly
and modern scientists with keeping up to date with the most recent advances and
new improvements in this speedy, high-influence field of video emotion
recognition.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Breast Cancer classification by adaptive weighted average ensemble of previously trained models
Abstract: Breast cancer is a serious disease that inflicts millions of people each
year, and the number of cases is increasing. Early detection is the best way to
reduce the impact of the disease. Researchers have developed many techniques to
detect breast cancer, including the use of histopathology images in CAD
systems. This research proposes a technique that combine already fully trained
model using adaptive average ensemble, this is different from the literature
which uses average ensemble before training and the average ensemble is trained
simultaneously. Our approach is different because it used adaptive average
ensemble after training which has increased the performance of evaluation
metrics. It averages the outputs of every trained model, and every model will
have weight according to its accuracy. The accuracy in the adaptive weighted
ensemble model has achieved 98% where the accuracy has increased by 1 percent
which is better than the best participating model in the ensemble which was
97%. Also, it decreased the numbers of false positive and false negative and
enhanced the performance metrics.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Generative Input: Towards Next-Generation Input Methods Paradigm
Abstract: Since the release of ChatGPT, generative models have achieved tremendous
success and become the de facto approach for various NLP tasks. However, its
application in the field of input methods remains under-explored. Many neural
network approaches have been applied to the construction of Chinese input
method engines(IMEs).Previous research often assumed that the input pinyin was
correct and focused on Pinyin-to-character(P2C) task, which significantly falls
short of meeting users' demands. Moreover, previous research could not leverage
user feedback to optimize the model and provide personalized results. In this
study, we propose a novel Generative Input paradigm named GeneInput. It uses
prompts to handle all input scenarios and other intelligent auxiliary input
functions, optimizing the model with user feedback to deliver personalized
results. The results demonstrate that we have achieved state-of-the-art
performance for the first time in the Full-mode Key-sequence to
Characters(FK2C) task. We propose a novel reward model training method that
eliminates the need for additional manual annotations and the performance
surpasses GPT-4 in tasks involving intelligent association and conversational
assistance. Compared to traditional paradigms, GeneInput not only demonstrates
superior performance but also exhibits enhanced robustness, scalability, and
online learning capabilities.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: On The Truthfulness of 'Surprisingly Likely' Responses of Large Language Models
Abstract: The surprisingly likely criterion in the seminal work of Prelec (the Bayesian
Truth Serum) guarantees truthfulness in a game-theoretic multi-agent setting,
by rewarding rational agents to maximise the expected information gain with
their answers w.r.t. their probabilistic beliefs. We investigate the relevance
of a similar criterion for responses of LLMs. We hypothesize that if the
surprisingly likely criterion works in LLMs, under certain conditions, the
responses that maximize the reward under this criterion should be more accurate
than the responses that only maximize the posterior probability. Using
benchmarks including the TruthfulQA benchmark and using openly available LLMs:
GPT-2 and LLaMA-2, we show that the method indeed improves the accuracy
significantly (for example, upto 24 percentage points aggregate improvement on
TruthfulQA and upto 70 percentage points improvement on individual categories
of questions).

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
Abstract: We present FoundationPose, a unified foundation model for 6D object pose
estimation and tracking, supporting both model-based and model-free setups. Our
approach can be instantly applied at test-time to a novel object without
fine-tuning, as long as its CAD model is given, or a small number of reference
images are captured. We bridge the gap between these two setups with a neural
implicit representation that allows for effective novel view synthesis, keeping
the downstream pose estimation modules invariant under the same unified
framework. Strong generalizability is achieved via large-scale synthetic
training, aided by a large language model (LLM), a novel transformer-based
architecture, and contrastive learning formulation. Extensive evaluation on
multiple public datasets involving challenging scenarios and objects indicate
our unified approach outperforms existing methods specialized for each task by
a large margin. In addition, it even achieves comparable results to
instance-level methods despite the reduced assumptions. Project page:
https://nvlabs.github.io/FoundationPose/

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Verification of Neural Reachable Tubes via Scenario Optimization and Conformal Prediction
Abstract: Learning-based approaches for controlling safety-critical systems are rapidly
growing in popularity; thus, it is important to assure their performance and
safety. Hamilton-Jacobi (HJ) reachability analysis is a popular formal
verification tool for providing such guarantees, since it can handle general
nonlinear system dynamics, bounded adversarial system disturbances, and state
and input constraints. However, its computational and memory complexity scales
exponentially with the state dimension, making it intractable for large-scale
systems. To overcome this challenge, neural approaches, such as DeepReach, have
been used to synthesize reachable tubes and safety controllers for
high-dimensional systems. However, verifying these neural reachable tubes
remains challenging. In this work, we propose two verification methods, based
on robust scenario optimization and conformal prediction, to provide
probabilistic safety guarantees for neural reachable tubes. Our methods allow a
direct trade-off between resilience to outlier errors in the neural tube, which
are inevitable in a learning-based approach, and the strength of the
probabilistic safety guarantee. Furthermore, we show that split conformal
prediction, a widely used method in the machine learning community for
uncertainty quantification, reduces to a scenario-based approach, making the
two methods equivalent not only for verification of neural reachable tubes but
also more generally. To our knowledge, our proof is the first in the literature
to show a strong relationship between conformal prediction and scenario
optimization. Finally, we propose an outlier-adjusted verification approach
that uses the error distribution in neural reachable tubes to recover greater
safe volumes. We demonstrate the efficacy of the proposed approaches for the
high-dimensional problems of multi-vehicle collision avoidance and rocket
landing with no-go zones.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation
Abstract: In applying reinforcement learning (RL) to high-stakes domains, quantitative
and qualitative evaluation using observational data can help practitioners
understand the generalization performance of new policies. However, this type
of off-policy evaluation (OPE) is inherently limited since offline data may not
reflect the distribution shifts resulting from the application of new policies.
On the other hand, online evaluation by collecting rollouts according to the
new policy is often infeasible, as deploying new policies in these domains can
be unsafe. In this work, we propose a semi-offline evaluation framework as an
intermediate step between offline and online evaluation, where human users
provide annotations of unobserved counterfactual trajectories. While tempting
to simply augment existing data with such annotations, we show that this naive
approach can lead to biased results. Instead, we design a new family of OPE
estimators based on importance sampling (IS) and a novel weighting scheme that
incorporate counterfactual annotations without introducing additional bias. We
analyze the theoretical properties of our approach, showing its potential to
reduce both bias and variance compared to standard IS estimators. Our analyses
reveal important practical considerations for handling biased, noisy, or
missing annotations. In a series of proof-of-concept experiments involving
bandits and a healthcare-inspired simulator, we demonstrate that our approach
outperforms purely offline IS estimators and is robust to imperfect
annotations. Our framework, combined with principled human-centered design of
annotation solicitation, can enable the application of RL in high-stakes
domains.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Interpreting Pretrained Language Models via Concept Bottlenecks
Abstract: Pretrained language models (PLMs) have made significant strides in various
natural language processing tasks. However, the lack of interpretability due to
their ``black-box'' nature poses challenges for responsible implementation.
Although previous studies have attempted to improve interpretability by using,
e.g., attention weights in self-attention layers, these weights often lack
clarity, readability, and intuitiveness. In this research, we propose a novel
approach to interpreting PLMs by employing high-level, meaningful concepts that
are easily understandable for humans. For example, we learn the concept of
``Food'' and investigate how it influences the prediction of a model's
sentiment towards a restaurant review. We introduce C$^3$M, which combines
human-annotated and machine-generated concepts to extract hidden neurons
designed to encapsulate semantically meaningful and task-specific concepts.
Through empirical evaluations on real-world datasets, we manifest that our
approach offers valuable insights to interpret PLM behavior, helps diagnose
model failures, and enhances model robustness amidst noisy concept labels.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: An Empirical Bayes Framework for Open-Domain Dialogue Generation
Abstract: To engage human users in meaningful conversation, open-domain dialogue agents
are required to generate diverse and contextually coherent dialogue. Despite
recent advancements, which can be attributed to the usage of pretrained
language models, the generation of diverse and coherent dialogue remains an
open research problem. A popular approach to address this issue involves the
adaptation of variational frameworks. However, while these approaches
successfully improve diversity, they tend to compromise on contextual
coherence. Hence, we propose the Bayesian Open-domain Dialogue with Empirical
Bayes (BODEB) framework, an empirical bayes framework for constructing an
Bayesian open-domain dialogue agent by leveraging pretrained parameters to
inform the prior and posterior parameter distributions. Empirical results show
that BODEB achieves better results in terms of both diversity and coherence
compared to variational frameworks.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Social, Legal, Ethical, Empathetic, and Cultural Rules: Compilation and Reasoning (Extended Version)
Abstract: The rise of AI-based and autonomous systems is raising concerns and
apprehension due to potential negative repercussions stemming from their
behavior or decisions. These systems must be designed to comply with the human
contexts in which they will operate. To this extent, Townsend et al. (2022)
introduce the concept of SLEEC (social, legal, ethical, empathetic, or
cultural) rules that aim to facilitate the formulation, verification, and
enforcement of the rules AI-based and autonomous systems should obey. They lay
out a methodology to elicit them and to let philosophers, lawyers, domain
experts, and others to formulate them in natural language. To enable their
effective use in AI systems, it is necessary to translate these rules
systematically into a formal language that supports automated reasoning. In
this study, we first conduct a linguistic analysis of the SLEEC rules pattern,
which justifies the translation of SLEEC rules into classical logic. Then we
investigate the computational complexity of reasoning about SLEEC rules and
show how logical programming frameworks can be employed to implement SLEEC
rules in practical scenarios. The result is a readily applicable strategy for
implementing AI systems that conform to norms expressed as SLEEC rules.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Evaluating Supervision Levels Trade-Offs for Infrared-Based People Counting
Abstract: Object detection models are commonly used for people counting (and
localization) in many applications but require a dataset with costly bounding
box annotations for training. Given the importance of privacy in people
counting, these models rely more and more on infrared images, making the task
even harder. In this paper, we explore how weaker levels of supervision can
affect the performance of deep person counting architectures for image
classification and point-level localization. Our experiments indicate that
counting people using a CNN Image-Level model achieves competitive results with
YOLO detectors and point-level models, yet provides a higher frame rate and a
similar amount of model parameters.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy
Abstract: Sensors are commonly deployed to perceive the environment. However, due to
the high cost, sensors are usually sparsely deployed. Kriging is the tailored
task to infer the unobserved nodes (without sensors) using the observed source
nodes (with sensors). The essence of kriging task is transferability. Recently,
several inductive spatio-temporal kriging methods have been proposed based on
graph neural networks, being trained based on a graph built on top of observed
nodes via pretext tasks such as masking nodes out and reconstructing them.
However, the graph in training is inevitably much sparser than the graph in
inference that includes all the observed and unobserved nodes. The learned
pattern cannot be well generalized for inference, denoted as graph gap. To
address this issue, we first present a novel Increment training strategy:
instead of masking nodes (and reconstructing them), we add virtual nodes into
the training graph so as to mitigate the graph gap issue naturally.
Nevertheless, the empty-shell virtual nodes without labels could have
bad-learned features and lack supervision signals. To solve these issues, we
pair each virtual node with its most similar observed node and fuse their
features together; to enhance the supervision signal, we construct reliable
pseudo labels for virtual nodes. As a result, the learned pattern of virtual
nodes could be safely transferred to real unobserved nodes for reliable
kriging. We name our new Kriging model with Increment Training Strategy as
KITS. Extensive experiments demonstrate that KITS consistently outperforms
existing kriging methods by large margins, e.g., the improvement over MAE score
could be as high as 18.33%.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Local Universal Rule-based Explanations
Abstract: Explainable artificial intelligence (XAI) is one of the most intensively
developed are of AI in recent years. It is also one of the most fragmented one
with multiple methods that focus on different aspects of explanations. This
makes difficult to obtain the full spectrum of explanation at once in a compact
and consistent way. To address this issue, we present Local Universal Explainer
(LUX) that is a rule-based explainer which can generate factual, counterfactual
and visual explanations. It is based on a modified version of decision tree
algorithms that allows for oblique splits and integration with feature
importance XAI methods such as SHAP or LIME. It does not use data generation in
opposite to other algorithms, but is focused on selecting local concepts in a
form of high-density clusters of real data that have the highest impact on
forming the decision boundary of the explained model. We tested our method on
real and synthetic datasets and compared it with state-of-the-art rule-based
explainers such as LORE, EXPLAN and Anchor. Our method outperforms currently
existing approaches in terms of simplicity, global fidelity and
representativeness.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Signal Temporal Logic-Guided Apprenticeship Learning
Abstract: Apprenticeship learning crucially depends on effectively learning rewards,
and hence control policies from user demonstrations. Of particular difficulty
is the setting where the desired task consists of a number of sub-goals with
temporal dependencies. The quality of inferred rewards and hence policies are
typically limited by the quality of demonstrations, and poor inference of these
can lead to undesirable outcomes. In this letter, we show how temporal logic
specifications that describe high level task objectives, are encoded in a graph
to define a temporal-based metric that reasons about behaviors of demonstrators
and the learner agent to improve the quality of inferred rewards and policies.
Through experiments on a diverse set of robot manipulator simulations, we show
how our framework overcomes the drawbacks of prior literature by drastically
improving the number of demonstrations required to learn a control policy.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Nonlinear Multi-objective Reinforcement Learning with Provable Guarantees
Abstract: We describe RA-E3 (Reward-Aware Explicit Explore or Exploit), an algorithm
with provable guarantees for solving a single or multi-objective Markov
Decision Process (MDP) where we want to maximize the expected value of a
nonlinear function over accumulated rewards. This allows us to model
fairness-aware welfare optimization for multi-objective reinforcement learning
as well as risk-aware reinforcement learning with nonlinear Von
Neumann-Morgenstern utility functions in the single objective setting. RA-E3
extends the classic E3 algorithm that solves MDPs with scalar rewards and
linear preferences. We first state a distinct reward-aware version of value
iteration that calculates a non-stationary policy that is approximately optimal
for a given model of the environment. This sub-procedure is based on an
extended form of Bellman optimality for nonlinear optimization that explicitly
considers time and current accumulated reward. We then describe how to use this
optimization procedure in a larger algorithm that must simultaneously learn a
model of the environment. The algorithm learns an approximately optimal policy
in time that depends polynomially on the MDP size, desired approximation, and
smoothness of the nonlinear function, and exponentially on the number of
objectives.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Diffusion-C: Unveiling the Generative Challenges of Diffusion Models through Corrupted Data
Abstract: In our contemporary academic inquiry, we present "Diffusion-C," a
foundational methodology to analyze the generative restrictions of Diffusion
Models, particularly those akin to GANs, DDPM, and DDIM. By employing input
visual data that has been subjected to a myriad of corruption modalities and
intensities, we elucidate the performance characteristics of those Diffusion
Models. The noise component takes center stage in our analysis, hypothesized to
be a pivotal element influencing the mechanics of deep learning systems. In our
rigorous expedition utilizing Diffusion-C, we have discerned the following
critical observations: (I) Within the milieu of generative models under the
Diffusion taxonomy, DDPM emerges as a paragon, consistently exhibiting superior
performance metrics. (II) Within the vast spectrum of corruption frameworks,
the fog and fractal corruptions notably undermine the functional robustness of
both DDPM and DDIM. (III) The vulnerability of Diffusion Models to these
particular corruptions is significantly influenced by topological and
statistical similarities, particularly concerning the alignment between mean
and variance. This scholarly work highlights Diffusion-C's core understandings
regarding the impacts of various corruptions, setting the stage for future
research endeavors in the realm of generative models.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Transdisciplinary AI Education: The Confluence of Curricular and Community Needs in the Instruction of Artificial Intelligence
Abstract: The integration of artificial intelligence (AI) into education has the
potential to transform the way we learn and teach. In this paper, we examine
the current state of AI in education and explore the potential benefits and
challenges of incorporating this technology into the classroom. The approaches
currently available for AI education often present students with experiences
only focusing on discrete computer science concepts agnostic to a larger
curriculum. However, teaching AI must not be siloed or interdisciplinary.
Rather, AI instruction ought to be transdisciplinary, including connections to
the broad curriculum and community in which students are learning. This paper
delves into the AI program currently in development for Neom Community School
and the larger Education, Research, and Innovation Sector in Neom, Saudi Arabia
s new megacity under development. In this program, AI is both taught as a
subject and to learn other subjects within the curriculum through the school
systems International Baccalaureate (IB) approach, which deploys learning
through Units of Inquiry. This approach to education connects subjects across a
curriculum under one major guiding question at a time. The proposed method
offers a meaningful approach to introducing AI to students throughout these
Units of Inquiry, as it shifts AI from a subject that students like or not like
to a subject that is taught throughout the curriculum.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model
Abstract: Signal peptide (SP) is a short peptide located in the N-terminus of proteins.
It is essential to target and transfer transmembrane and secreted proteins to
correct positions. Compared with traditional experimental methods to identify
signal peptides, computational methods are faster and more efficient, which are
more practical for analyzing thousands or even millions of protein sequences,
especially for metagenomic data. Here we present Unbiased Organism-agnostic
Signal Peptide Network (USPNet), a signal peptide classification and cleavage
site prediction deep learning method that takes advantage of protein language
models. We propose to apply label distribution-aware margin loss to handle data
imbalance problems and use evolutionary information of protein to enrich
representation and overcome species information dependence.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: ReConTab: Regularized Contrastive Representation Learning for Tabular Data
Abstract: Representation learning stands as one of the critical machine learning
techniques across various domains. Through the acquisition of high-quality
features, pre-trained embeddings significantly reduce input space redundancy,
benefiting downstream pattern recognition tasks such as classification,
regression, or detection. Nonetheless, in the domain of tabular data, feature
engineering and selection still heavily rely on manual intervention, leading to
time-consuming processes and necessitating domain expertise. In response to
this challenge, we introduce ReConTab, a deep automatic representation learning
framework with regularized contrastive learning. Agnostic to any type of
modeling task, ReConTab constructs an asymmetric autoencoder based on the same
raw features from model inputs, producing low-dimensional representative
embeddings. Specifically, regularization techniques are applied for raw feature
selection. Meanwhile, ReConTab leverages contrastive learning to distill the
most pertinent information for downstream tasks. Experiments conducted on
extensive real-world datasets substantiate the framework's capacity to yield
substantial and robust performance improvements. Furthermore, we empirically
demonstrate that pre-trained embeddings can seamlessly integrate as easily
adaptable features, enhancing the performance of various traditional methods
such as XGBoost and Random Forest.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Computational Copyright: Towards A Royalty Model for AI Music Generation Platforms
Abstract: The advancement of generative AI has given rise to pressing copyright
challenges, particularly in music industry. This paper focuses on the economic
aspects of these challenges, emphasizing that the economic impact constitutes a
central issue in the copyright arena. The complexity of the black-box
generative AI technologies not only suggests but necessitates algorithmic
solutions. However, such solutions have been largely missing, leading to
regulatory challenges in this landscape. We aim to bridge the gap in current
approaches by proposing potential royalty models for revenue sharing on AI
music generation platforms. Our methodology involves a detailed analysis of
existing royalty models in platforms like Spotify and YouTube, and adapting
these to the unique context of AI-generated music. A significant challenge we
address is the attribution of AI-generated music to influential copyrighted
content in the training data. To this end, we present algorithmic solutions
employing data attribution techniques. Our experimental results verify the
effectiveness of these solutions. This research represents a pioneering effort
in integrating technical advancements with economic and legal considerations in
the field of generative AI, offering a computational copyright solution for the
challenges posed by the opaque nature of AI technologies.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization
Abstract: In this study, we introduce JarviX, a sophisticated data analytics framework.
JarviX is designed to employ Large Language Models (LLMs) to facilitate an
automated guide and execute high-precision data analyzes on tabular datasets.
This framework emphasizes the significance of varying column types,
capitalizing on state-of-the-art LLMs to generate concise data insight
summaries, propose relevant analysis inquiries, visualize data effectively, and
provide comprehensive explanations for results drawn from an extensive data
analysis pipeline. Moreover, JarviX incorporates an automated machine learning
(AutoML) pipeline for predictive modeling. This integration forms a
comprehensive and automated optimization cycle, which proves particularly
advantageous for optimizing machine configuration. The efficacy and
adaptability of JarviX are substantiated through a series of practical use case
studies.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Rethinking Decision Transformer via Hierarchical Reinforcement Learning
Abstract: Decision Transformer (DT) is an innovative algorithm leveraging recent
advances of the transformer architecture in reinforcement learning (RL).
However, a notable limitation of DT is its reliance on recalling trajectories
from datasets, losing the capability to seamlessly stitch sub-optimal
trajectories together. In this work we introduce a general sequence modeling
framework for studying sequential decision making through the lens of
Hierarchical RL. At the time of making decisions, a high-level policy first
proposes an ideal prompt for the current state, a low-level policy subsequently
generates an action conditioned on the given prompt. We show DT emerges as a
special case of this framework with certain choices of high-level and low-level
policies, and discuss the potential failure of these choices. Inspired by these
observations, we study how to jointly optimize the high-level and low-level
policies to enable the stitching ability, which further leads to the
development of new offline RL algorithms. Our empirical results clearly show
that the proposed algorithms significantly surpass DT on several control and
navigation benchmarks. We hope our contributions can inspire the integration of
transformer architectures within the field of RL.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Medical Image Retrieval Using Pretrained Embeddings
Abstract: A wide range of imaging techniques and data formats available for medical
images make accurate retrieval from image databases challenging.
  Efficient retrieval systems are crucial in advancing medical research,
enabling large-scale studies and innovative diagnostic tools. Thus, addressing
the challenges of medical image retrieval is essential for the continued
enhancement of healthcare and research.
  In this study, we evaluated the feasibility of employing four
state-of-the-art pretrained models for medical image retrieval at modality,
body region, and organ levels and compared the results of two similarity
indexing approaches. Since the employed networks take 2D images, we analyzed
the impacts of weighting and sampling strategies to incorporate 3D information
during retrieval of 3D volumes. We showed that medical image retrieval is
feasible using pretrained networks without any additional training or
fine-tuning steps. Using pretrained embeddings, we achieved a recall of 1 for
various tasks at modality, body region, and organ level.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning
Abstract: The burgeoning navigation services using digital maps provide great
convenience to drivers. Nevertheless, the presence of anomalies in lane
rendering map images occasionally introduces potential hazards, as such
anomalies can be misleading to human drivers and consequently contribute to
unsafe driving conditions. In response to this concern and to accurately and
effectively detect the anomalies, this paper transforms lane rendering image
anomaly detection into a classification problem and proposes a four-phase
pipeline consisting of data pre-processing, self-supervised pre-training with
the masked image modeling (MiM) method, customized fine-tuning using
cross-entropy based loss with label smoothing, and post-processing to tackle it
leveraging state-of-the-art deep learning techniques, especially those
involving Transformer models. Various experiments verify the effectiveness of
the proposed pipeline. Results indicate that the proposed pipeline exhibits
superior performance in lane rendering image anomaly detection, and notably,
the self-supervised pre-training with MiM can greatly enhance the detection
accuracy while significantly reducing the total training time. For instance,
employing the Swin Transformer with Uniform Masking as self-supervised
pretraining (Swin-Trans-UM) yielded a heightened accuracy at 94.77% and an
improved Area Under The Curve (AUC) score of 0.9743 compared with the pure Swin
Transformer without pre-training (Swin-Trans) with an accuracy of 94.01% and an
AUC of 0.9498. The fine-tuning epochs were dramatically reduced to 41 from the
original 280. In conclusion, the proposed pipeline, with its incorporation of
self-supervised pre-training using MiM and other advanced deep learning
techniques, emerges as a robust solution for enhancing the accuracy and
efficiency of lane rendering image anomaly detection in digital navigation
systems.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning
Abstract: Graph pooling methods have been widely used on downsampling graphs, achieving
impressive results on multiple graph-level tasks like graph classification and
graph generation. An important line called node dropping pooling aims at
exploiting learnable scoring functions to drop nodes with comparatively lower
significance scores. However, existing node dropping methods suffer from two
limitations: (1) for each pooled node, these models struggle to capture
long-range dependencies since they mainly take GNNs as the backbones; (2)
pooling only the highest-scoring nodes tends to preserve similar nodes, thus
discarding the affluent information of low-scoring nodes. To address these
issues, we propose a Graph Transformer Pooling method termed GTPool, which
introduces Transformer to node dropping pooling to efficiently capture
long-range pairwise interactions and meanwhile sample nodes diversely.
Specifically, we design a scoring module based on the self-attention mechanism
that takes both global context and local context into consideration, measuring
the importance of nodes more comprehensively. GTPool further utilizes a
diversified sampling method named Roulette Wheel Sampling (RWS) that is able to
flexibly preserve nodes across different scoring intervals instead of only
higher scoring nodes. In this way, GTPool could effectively obtain long-range
information and select more representative nodes. Extensive experiments on 11
benchmark datasets demonstrate the superiority of GTPool over existing popular
graph pooling methods.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Correction with Backtracking Reduces Hallucination in Summarization
Abstract: Abstractive summarization aims at generating natural language summaries of a
source document that are succinct while preserving the important elements.
Despite recent advances, neural text summarization models are known to be
susceptible to hallucinating (or more correctly confabulating), that is to
produce summaries with details that are not grounded in the source document. In
this paper, we introduce a simple yet efficient technique, CoBa, to reduce
hallucination in abstractive summarization. The approach is based on two steps:
hallucination detection and mitigation. We show that the former can be achieved
through measuring simple statistics about conditional word probabilities and
distance to context words. Further, we demonstrate that straight-forward
backtracking is surprisingly effective at mitigation. We thoroughly evaluate
the proposed method with prior art on three benchmark datasets for text
summarization. The results show that CoBa is effective and efficient in
reducing hallucination, and offers great adaptability and flexibility.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: MI-Gen: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Images
Abstract: Whole slide images are the foundation of digital pathology for the diagnosis
and treatment of carcinomas. Writing pathology reports is laborious and
error-prone for inexperienced pathologists. To reduce the workload and improve
clinical automation, we investigate how to generate pathology reports given
whole slide images. On the data end, we curated the largest WSI-text dataset
(TCGA-PathoText). In specific, we collected nearly 10000 high-quality WSI-text
pairs for visual-language models by recognizing and cleaning pathology reports
which narrate diagnostic slides in TCGA. On the model end, we propose the
multiple instance generative model (MI-Gen) which can produce pathology reports
for gigapixel WSIs. We benchmark our model on the largest subset of
TCGA-PathoText. Experimental results show our model can generate pathology
reports which contain multiple clinical clues. Furthermore, WSI-text prediction
can be seen as an approach of visual-language pre-training, which enables our
model to be transferred to downstream diagnostic tasks like carcinoma grading
and phenotyping. We observe that simple semantic extraction from the pathology
reports can achieve the best performance (0.838 of F1 score) on BRCA subtyping
without adding extra parameters or tricky fine-tuning. Our collected dataset
and related code will all be publicly available.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement
Abstract: Humans use abstract concepts for understanding instead of hard features.
Recent interpretability research has focused on human-centered concept
explanations of neural networks. Concept Activation Vectors (CAVs) estimate a
model's sensitivity and possible biases to a given concept. In this paper, we
extend CAVs from post-hoc analysis to ante-hoc training in order to reduce
model bias through fine-tuning using an additional Concept Loss. Concepts were
defined on the final layer of the network in the past. We generalize it to
intermediate layers using class prototypes. This facilitates class learning in
the last convolution layer, which is known to be most informative. We also
introduce Concept Distillation to create richer concepts using a pre-trained
knowledgeable model as the teacher. Our method can sensitize or desensitize a
model towards concepts. We show applications of concept-sensitive training to
debias several classification problems. We also use concepts to induce prior
knowledge into IID, a reconstruction problem. Concept-sensitive training can
improve model interpretability, reduce biases, and induce prior knowledge.
Please visit https://avani17101.github.io/Concept-Distilllation/ for code and
more details.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-Learning
Abstract: We conduct investigations on clinical text machine translation by examining
multilingual neural network models using deep learning such as Transformer
based structures. Furthermore, to address the language resource imbalance
issue, we also carry out experiments using a transfer learning methodology
based on massive multilingual pre-trained language models (MMPLMs). The
experimental results on three subtasks including 1) clinical case (CC), 2)
clinical terminology (CT), and 3) ontological concept (OC) show that our models
achieved top-level performances in the ClinSpEn-2022 shared task on
English-Spanish clinical domain data. Furthermore, our expert-based human
evaluations demonstrate that the small-sized pre-trained language model (PLM)
won over the other two extra-large language models by a large margin, in the
clinical domain fine-tuning, which finding was never reported in the field.
Finally, the transfer learning method works well in our experimental setting
using the WMT21fb model to accommodate a new language space Spanish that was
not seen at the pre-training stage within WMT21fb itself, which deserves more
exploitation for clinical knowledge transformation, e.g. to investigate into
more languages. These research findings can shed some light on domain-specific
machine translation development, especially in clinical and healthcare fields.
Further research projects can be carried out based on our work to improve
healthcare text analytics and knowledge transformation.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Apollo's Oracle: Retrieval-Augmented Reasoning in Multi-Agent Debates
Abstract: Multi-agent debate systems are designed to derive accurate and consistent
conclusions through adversarial interactions among agents. However, these
systems often encounter challenges due to cognitive constraints, manifesting as
(1) agents' obstinate adherence to incorrect viewpoints and (2) their
propensity to abandon correct viewpoints. These issues are primarily
responsible for the ineffectiveness of such debates. Addressing the challenge
of cognitive constraints, we introduce a novel framework, the Multi-Agent
Debate with Retrieval Augmented (MADRA). MADRA incorporates retrieval of prior
knowledge into the debate process, effectively breaking cognitive constraints
and enhancing the agents' reasoning capabilities. Furthermore, we have
developed a self-selection module within this framework, enabling agents to
autonomously select pertinent evidence, thereby minimizing the impact of
irrelevant or noisy data. We have comprehensively tested and analyzed MADRA
across six diverse datasets. The experimental results demonstrate that our
approach significantly enhances performance across various tasks, proving the
effectiveness of our proposed method.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Compositional Chain-of-Thought Prompting for Large Multimodal Models
Abstract: The combination of strong visual backbones and Large Language Model (LLM)
reasoning has led to Large Multimodal Models (LMMs) becoming the current
standard for a wide range of vision and language (VL) tasks. However, recent
research has shown that even the most advanced LMMs still struggle to capture
aspects of compositional visual reasoning, such as attributes and relationships
between objects. One solution is to utilize scene graphs (SGs)--a formalization
of objects and their relations and attributes that has been extensively used as
a bridge between the visual and textual domains. Yet, scene graph data requires
scene graph annotations, which are expensive to collect and thus not easily
scalable. Moreover, finetuning an LMM based on SG data can lead to catastrophic
forgetting of the pretraining objective. To overcome this, inspired by
chain-of-thought methods, we propose Compositional Chain-of-Thought (CCoT), a
novel zero-shot Chain-of-Thought prompting method that utilizes SG
representations in order to extract compositional knowledge from an LMM.
Specifically, we first generate an SG using the LMM, and then use that SG in
the prompt to produce a response. Through extensive experiments, we find that
the proposed CCoT approach not only improves LMM performance on several vision
and language VL compositional benchmarks but also improves the performance of
several popular LMMs on general multimodal benchmarks, without the need for
fine-tuning or annotated ground-truth SGs.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: LMD: Faster Image Reconstruction with Latent Masking Diffusion
Abstract: As a class of fruitful approaches, diffusion probabilistic models (DPMs) have
shown excellent advantages in high-resolution image reconstruction. On the
other hand, masked autoencoders (MAEs), as popular self-supervised vision
learners, have demonstrated simpler and more effective image reconstruction and
transfer capabilities on downstream tasks. However, they all require extremely
high training costs, either due to inherent high temporal-dependence (i.e.,
excessively long diffusion steps) or due to artificially low spatial-dependence
(i.e., human-formulated high mask ratio, such as 0.75). To the end, this paper
presents LMD, a faster image reconstruction framework with latent masking
diffusion. First, we propose to project and reconstruct images in latent space
through a pre-trained variational autoencoder, which is theoretically more
efficient than in the pixel-based space. Then, we combine the advantages of
MAEs and DPMs to design a progressive masking diffusion model, which gradually
increases the masking proportion by three different schedulers and reconstructs
the latent features from simple to difficult, without sequentially performing
denoising diffusion as in DPMs or using fixed high masking ratio as in MAEs, so
as to alleviate the high training time-consumption predicament. Our approach
allows for learning high-capacity models and accelerate their training (by 3x
or more) and barely reduces the original accuracy. Inference speed in
downstream tasks also significantly outperforms the previous approaches.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Do Physicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generation
Abstract: This study examines the effect of prompt engineering on the performance of
Large Language Models (LLMs) in clinical note generation. We introduce an
Automatic Prompt Optimization (APO) framework to refine initial prompts and
compare the outputs of medical experts, non-medical experts, and APO-enhanced
GPT3.5 and GPT4. Results highlight GPT4 APO's superior performance in
standardizing prompt quality across clinical note sections. A human-in-the-loop
approach shows that experts maintain content quality post-APO, with a
preference for their own modifications, suggesting the value of expert
customization. We recommend a two-phase optimization process, leveraging
APO-GPT4 for consistency and expert input for personalization.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation
Abstract: Mobile health programs are becoming an increasingly popular medium for
dissemination of health information among beneficiaries in less privileged
communities. Kilkari is one of the world's largest mobile health programs which
delivers time sensitive audio-messages to pregnant women and new mothers. We
have been collaborating with ARMMAN, a non-profit in India which operates the
Kilkari program, to identify bottlenecks to improve the efficiency of the
program. In particular, we provide an initial analysis of the trajectories of
beneficiaries' interaction with the mHealth program and examine elements of the
program that can be potentially enhanced to boost its success. We cluster the
cohort into different buckets based on listenership so as to analyze
listenership patterns for each group that could help boost program success. We
also demonstrate preliminary results on using historical data in a time-series
prediction to identify beneficiary dropouts and enable NGOs in devising timely
interventions to strengthen beneficiary retention.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation
Abstract: Session-based recommendation (SBR) aims to predict the next item at a certain
time point based on anonymous user behavior sequences. Existing methods
typically model session representation based on simple item transition
information. However, since session-based data consists of limited users'
short-term interactions, modeling session representation by capturing fixed
item transition information from a single dimension suffers from data sparsity.
In this paper, we propose a novel contrastive multi-level graph neural networks
(CM-GNN) to better exploit complex and high-order item transition information.
Specifically, CM-GNN applies local-level graph convolutional network (L-GCN)
and global-level network (G-GCN) on the current session and all the sessions
respectively, to effectively capture pairwise relations over all the sessions
by aggregation strategy. Meanwhile, CM-GNN applies hyper-level graph
convolutional network (H-GCN) to capture high-order information among all the
item transitions. CM-GNN further introduces an attention-based fusion module to
learn pairwise relation-based session representation by fusing the item
representations generated by L-GCN and G-GCN. CM-GNN averages the item
representations obtained by H-GCN to obtain high-order relation-based session
representation. Moreover, to convert the high-order item transition information
into the pairwise relation-based session representation, CM-GNN maximizes the
mutual information between the representations derived from the fusion module
and the average pool layer by contrastive learning paradigm. We conduct
extensive experiments on multiple widely used benchmark datasets to validate
the efficacy of the proposed method. The encouraging results demonstrate that
our proposed method outperforms the state-of-the-art SBR techniques.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: The Case for Universal Basic Computing Power
Abstract: The Universal Basic Computing Power (UBCP) initiative ensures global, free
access to a set amount of computing power specifically for AI research and
development (R&D). This initiative comprises three key elements. First, UBCP
must be cost free, with its usage limited to AI R&D and minimal additional
conditions. Second, UBCP should continually incorporate the state of the art AI
advancements, including efficiently distilled, compressed, and deployed
training data, foundational models, benchmarks, and governance tools. Lastly,
it's essential for UBCP to be universally accessible, ensuring convenience for
all users. We urge major stakeholders in AI development large platforms, open
source contributors, and policymakers to prioritize the UBCP initiative.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Contractive error feedback for gradient compression
Abstract: On-device memory concerns in distributed deep learning have become severe due
to (i) the growth of model size in multi-GPU training, and (ii) the wide
adoption of deep neural networks for federated learning on IoT devices which
have limited storage. In such settings, communication efficient optimization
methods are attractive alternatives, however they still struggle with memory
issues. To tackle these challenges, we propose an communication efficient
method called contractive error feedback (ConEF). As opposed to SGD with
error-feedback (EFSGD) that inefficiently manages memory, ConEF obtains the
sweet spot of convergence and memory usage, and achieves communication
efficiency by leveraging biased and all-reducable gradient compression. We
empirically validate ConEF on various learning tasks that include image
classification, language modeling, and machine translation and observe that
ConEF saves 80\% - 90\% of the extra memory in EFSGD with almost no loss on
test performance, while also achieving 1.3x - 5x speedup of SGD. Through our
work, we also demonstrate the feasibility and convergence of ConEF to clear up
the theoretical barrier of integrating ConEF to popular memory efficient
frameworks such as ZeRO-3.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Distributed Learning of Mixtures of Experts
Abstract: In modern machine learning problems we deal with datasets that are either
distributed by nature or potentially large for which distributing the
computations is usually a standard way to proceed, since centralized algorithms
are in general ineffective. We propose a distributed learning approach for
mixtures of experts (MoE) models with an aggregation strategy to construct a
reduction estimator from local estimators fitted parallelly to distributed
subsets of the data. The aggregation is based on an optimal minimization of an
expected transportation divergence between the large MoE composed of local
estimators and the unknown desired MoE model. We show that the provided
reduction estimator is consistent as soon as the local estimators to be
aggregated are consistent, and its construction is performed by a proposed
majorization-minimization (MM) algorithm that is computationally effective. We
study the statistical and numerical properties for the proposed reduction
estimator on experiments that demonstrate its performance compared to namely
the global estimator constructed in a centralized way from the full dataset.
For some situations, the computation time is more than ten times faster, for a
comparable performance. Our source codes are publicly available on Github.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Challenging Common Assumptions in Multi-task Learning
Abstract: While multi-task learning (MTL) has gained significant attention in recent
years, its underlying mechanisms remain poorly understood. Recent methods did
not yield consistent performance improvements over single task learning (STL)
baselines, underscoring the importance of gaining more profound insights about
challenges specific to MTL. In our study, we challenge common assumptions in
MTL in the context of STL: First, the choice of optimizer has only been mildly
investigated in MTL. We show the pivotal role of common STL tools such as the
Adam optimizer in MTL. We deduce the effectiveness of Adam to its partial
loss-scale invariance. Second, the notion of gradient conflicts has often been
phrased as a specific problem in MTL. We delve into the role of gradient
conflicts in MTL and compare it to STL. For angular gradient alignment we find
no evidence that this is a unique problem in MTL. We emphasize differences in
gradient magnitude as the main distinguishing factor. Lastly, we compare the
transferability of features learned through MTL and STL on common image
corruptions, and find no conclusive evidence that MTL leads to superior
transferability. Overall, we find surprising similarities between STL and MTL
suggesting to consider methods from both fields in a broader context.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: General Phrase Debiaser: Debiasing Masked Language Models at a Multi-Token Level
Abstract: The social biases and unwelcome stereotypes revealed by pretrained language
models are becoming obstacles to their application. Compared to numerous
debiasing methods targeting word level, there has been relatively less
attention on biases present at phrase level, limiting the performance of
debiasing in discipline domains. In this paper, we propose an automatic
multi-token debiasing pipeline called \textbf{General Phrase Debiaser}, which
is capable of mitigating phrase-level biases in masked language models.
Specifically, our method consists of a \textit{phrase filter stage} that
generates stereotypical phrases from Wikipedia pages as well as a \textit{model
debias stage} that can debias models at the multi-token level to tackle bias
challenges on phrases. The latter searches for prompts that trigger model's
bias, and then uses them for debiasing. State-of-the-art results on standard
datasets and metrics show that our approach can significantly reduce gender
biases on both career and multiple disciplines, across models with varying
parameter sizes.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers' Workflows
Abstract: Prototyping AI applications is notoriously difficult. While large language
model (LLM) prompting has dramatically lowered the barriers to AI prototyping,
designers are still prototyping AI functionality and UI separately. We
investigate how coupling prompt and UI design affects designers' workflows.
Grounding this research, we developed PromptInfuser, a Figma plugin that
enables users to create semi-functional mockups, by connecting UI elements to
the inputs and outputs of prompts. In a study with 14 designers, we compare
PromptInfuser to designers' current AI-prototyping workflow. PromptInfuser was
perceived to be significantly more useful for communicating product ideas, more
capable of producing prototypes that realistically represent the envisioned
artifact, more efficient for prototyping, and more helpful for anticipating UI
issues and technical constraints. PromptInfuser encouraged iteration over
prompt and UI together, which helped designers identify UI and prompt
incompatibilities and reflect upon their total solution. Together, these
findings inform future systems for prototyping AI applications.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos
Abstract: The gigapixel scale of whole slide images (WSIs) poses a challenge for
histopathology multi-modal chatbots, requiring a global WSI analysis for
diagnosis, compounding evidence from different WSI patches. Current visual
instruction datasets, generated through large language models, focus on
creating question/answer pairs for individual image patches, which may lack
diagnostic capacity on their own in histopathology, further complicated by the
absence of spatial grounding in histopathology image captions. To bridge this
gap, we introduce Quilt-Instruct, a large-scale dataset of 107,131
histopathology-specific instruction question/answer pairs, that is collected by
leveraging educational histopathology videos from YouTube, which provides
spatial localization of captions by automatically extracting narrators' cursor
movements. In addition, we provide contextual reasoning by extracting diagnosis
and supporting facts from the entire video content to guide the extrapolative
reasoning of GPT-4. Using Quilt-Instruct, we train Quilt-LLaVA, which can
reason beyond the given single image patch, enabling diagnostic reasoning and
the capability of spatial awareness. To evaluate Quilt-LLaVA, we propose a
comprehensive evaluation dataset created from 985 images and 1283
human-generated question-answers. We also thoroughly evaluate Quilt-LLaVA using
public histopathology datasets, where Quilt-LLaVA significantly outperforms
SOTA by over 10% on relative GPT-4 score and 4% and 9% on open and closed set
VQA. Our code, data, and model are publicly available at quilt-llava.github.io.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: An Empirical Study of Benchmarking Chinese Aspect Sentiment Quad Prediction
Abstract: Aspect sentiment quad prediction (ASQP) is a critical subtask of aspect-level
sentiment analysis. Current ASQP datasets are characterized by their small size
and low quadruple density, which hinders technical development. To expand
capacity, we construct two large Chinese ASQP datasets crawled from multiple
online platforms. The datasets hold several significant characteristics: larger
size (each with 10,000+ samples) and rich aspect categories, more words per
sentence, and higher density than existing ASQP datasets. Moreover, we are the
first to evaluate the performance of Generative Pre-trained Transformer (GPT)
series models on ASQP and exhibit potential issues. The experiments with
state-of-the-art ASQP baselines underscore the need to explore additional
techniques to address ASQP, as well as the importance of further investigation
into methods to improve the performance of GPTs.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Alignment is not sufficient to prevent large language models from generating harmful information: A psychoanalytic perspective
Abstract: Large Language Models (LLMs) are central to a multitude of applications but
struggle with significant risks, notably in generating harmful content and
biases. Drawing an analogy to the human psyche's conflict between evolutionary
survival instincts and societal norm adherence elucidated in Freud's
psychoanalysis theory, we argue that LLMs suffer a similar fundamental
conflict, arising between their inherent desire for syntactic and semantic
continuity, established during the pre-training phase, and the post-training
alignment with human values. This conflict renders LLMs vulnerable to
adversarial attacks, wherein intensifying the models' desire for continuity can
circumvent alignment efforts, resulting in the generation of harmful
information. Through a series of experiments, we first validated the existence
of the desire for continuity in LLMs, and further devised a straightforward yet
powerful technique, such as incomplete sentences, negative priming, and
cognitive dissonance scenarios, to demonstrate that even advanced LLMs struggle
to prevent the generation of harmful information. In summary, our study
uncovers the root of LLMs' vulnerabilities to adversarial attacks, hereby
questioning the efficacy of solely relying on sophisticated alignment methods,
and further advocates for a new training idea that integrates modal concepts
alongside traditional amodal concepts, aiming to endow LLMs with a more nuanced
understanding of real-world contexts and ethical considerations.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: I-PHYRE: Interactive Physical Reasoning
Abstract: Current evaluation protocols predominantly assess physical reasoning in
stationary scenes, creating a gap in evaluating agents' abilities to interact
with dynamic events. While contemporary methods allow agents to modify initial
scene configurations and observe consequences, they lack the capability to
interact with events in real time. To address this, we introduce I-PHYRE, a
framework that challenges agents to simultaneously exhibit intuitive physical
reasoning, multi-step planning, and in-situ intervention. Here, intuitive
physical reasoning refers to a quick, approximate understanding of physics to
address complex problems; multi-step denotes the need for extensive sequence
planning in I-PHYRE, considering each intervention can significantly alter
subsequent choices; and in-situ implies the necessity for timely object
manipulation within a scene, where minor timing deviations can result in task
failure. We formulate four game splits to scrutinize agents' learning and
generalization of essential principles of interactive physical reasoning,
fostering learning through interaction with representative scenarios. Our
exploration involves three planning strategies and examines several supervised
and reinforcement agents' zero-shot generalization proficiency on I-PHYRE. The
outcomes highlight a notable gap between existing learning algorithms and human
performance, emphasizing the imperative for more research in enhancing agents
with interactive physical reasoning capabilities. The environment and baselines
will be made publicly available.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Going beyond persistent homology using persistent homology
Abstract: Representational limits of message-passing graph neural networks (MP-GNNs),
e.g., in terms of the Weisfeiler-Leman (WL) test for isomorphism, are well
understood. Augmenting these graph models with topological features via
persistent homology (PH) has gained prominence, but identifying the class of
attributed graphs that PH can recognize remains open. We introduce a novel
concept of color-separating sets to provide a complete resolution to this
important problem. Specifically, we establish the necessary and sufficient
conditions for distinguishing graphs based on the persistence of their
connected components, obtained from filter functions on vertex and edge colors.
Our constructions expose the limits of vertex- and edge-level PH, proving that
neither category subsumes the other. Leveraging these theoretical insights, we
propose RePHINE for learning topological features on graphs. RePHINE
efficiently combines vertex- and edge-level PH, achieving a scheme that is
provably more powerful than both. Integrating RePHINE into MP-GNNs boosts their
expressive power, resulting in gains over standard PH on several benchmarks for
graph classification.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Evaluating Neighbor Explainability for Graph Neural Networks
Abstract: Explainability in Graph Neural Networks (GNNs) is a new field growing in the
last few years. In this publication we address the problem of determining how
important is each neighbor for the GNN when classifying a node and how to
measure the performance for this specific task. To do this, various known
explainability methods are reformulated to get the neighbor importance and four
new metrics are presented. Our results show that there is almost no difference
between the explanations provided by gradient-based techniques in the GNN
domain. In addition, many explainability techniques failed to identify
important neighbors when GNNs without self-loops are used.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Grounding Foundation Models through Federated Transfer Learning: A General Framework
Abstract: Foundation Models (FMs) such as GPT-4 encoded with vast knowledge and
powerful emergent abilities have achieved remarkable success in various natural
language processing and computer vision tasks. Grounding FMs by adapting them
to domain-specific tasks or augmenting them with domain-specific knowledge
enables us to exploit the full potential of FMs. However, grounding FMs faces
several challenges, stemming primarily from constrained computing resources,
data privacy, model heterogeneity, and model ownership. Federated Transfer
Learning (FTL), the combination of federated learning and transfer learning,
provides promising solutions to address these challenges. In recent years, the
need for grounding FMs leveraging FTL, coined FTL-FM, has arisen strongly in
both academia and industry. Motivated by the strong growth in FTL-FM research
and the potential impact of FTL-FM on industrial applications, we propose an
FTL-FM framework that formulates problems of grounding FMs in the federated
learning setting, construct a detailed taxonomy based on the FTL-FM framework
to categorize state-of-the-art FTL-FM works, and comprehensively overview
FTL-FM works based on the proposed taxonomy. We also establish correspondences
between FTL-FM and conventional phases of adapting FM so that FM practitioners
can align their research works with FTL-FM. In addition, we overview advanced
efficiency-improving and privacy-preserving techniques because efficiency and
privacy are critical concerns in FTL-FM. Last, we discuss opportunities and
future research directions of FTL-FM.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Knowledge-Based Support for Adhesive Selection: Will it Stick?
Abstract: As the popularity of adhesive joints in industry increases, so does the need
for tools to support the process of selecting a suitable adhesive. While some
such tools already exist, they are either too limited in scope, or offer too
little flexibility in use. This work presents a more advanced tool, that was
developed together with a team of adhesive experts. We first extract the
experts' knowledge about this domain and formalize it in a Knowledge Base (KB).
The IDP-Z3 reasoning system can then be used to derive the necessary
functionality from this KB. Together with a user-friendly interactive
interface, this creates an easy-to-use tool capable of assisting the adhesive
experts. To validate our approach, we performed user testing in the form of
qualitative interviews. The experts are very positive about the tool, stating
that, among others, it will help save time and find more suitable adhesives.
Under consideration in Theory and Practice of Logic Programming (TPLP).

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network
Abstract: Federated Learning (FL) enables many resource-limited devices to train a
model collaboratively without data sharing. However, many existing works focus
on model-homogeneous FL, where the global and local models are the same size,
ignoring the inherently heterogeneous computational capabilities of different
devices and restricting resource-constrained devices from contributing to FL.
In this paper, we consider model-heterogeneous FL and propose Federated Partial
Model Training (FedPMT), where devices with smaller computational capabilities
work on partial models (subsets of the global model) and contribute to the
global model. Different from Dropout-based partial model generation, which
removes neurons in hidden layers at random, model training in FedPMT is
achieved from the back-propagation perspective. As such, all devices in FedPMT
prioritize the most crucial parts of the global model. Theoretical analysis
shows that the proposed partial model training design has a similar convergence
rate to the widely adopted Federated Averaging (FedAvg) algorithm,
$\mathcal{O}(1/T)$, with the sub-optimality gap enlarged by a constant factor
related to the model splitting design in FedPMT. Empirical results show that
FedPMT significantly outperforms the existing benchmark FedDrop. Meanwhile,
compared to the popular model-homogeneous benchmark, FedAvg, FedPMT reaches the
learning target in a shorter completion time, thus achieving a better trade-off
between learning accuracy and completion time.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Inspecting Explainability of Transformer Models with Additional Statistical Information
Abstract: Transformer becomes more popular in the vision domain in recent years so
there is a need for finding an effective way to interpret the Transformer model
by visualizing it. In recent work, Chefer et al. can visualize the Transformer
on vision and multi-modal tasks effectively by combining attention layers to
show the importance of each image patch. However, when applying to other
variants of Transformer such as the Swin Transformer, this method can not focus
on the predicted object. Our method, by considering the statistics of tokens in
layer normalization layers, shows a great ability to interpret the
explainability of Swin Transformer and ViT.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: You don't need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments
Abstract: The versatility of Large Language Models (LLMs) on natural language
understanding tasks has made them popular for research in social sciences. In
particular, to properly understand the properties and innate personas of LLMs,
researchers have performed studies that involve using prompts in the form of
questions that ask LLMs of particular opinions. In this study, we take a
cautionary step back and examine whether the current format of prompting
enables LLMs to provide responses in a consistent and robust manner. We first
construct a dataset that contains 693 questions encompassing 39 different
instruments of persona measurement on 115 persona axes. Additionally, we design
a set of prompts containing minor variations and examine LLM's capabilities to
generate accurate answers, as well as consistency variations to examine their
consistency towards simple perturbations such as switching the option order.
Our experiments on 15 different open-source LLMs reveal that even simple
perturbations are sufficient to significantly downgrade a model's
question-answering ability, and that most LLMs have low negation consistency.
Our results suggest that the currently widespread practice of prompting is
insufficient to accurately capture model perceptions, and we discuss potential
alternatives to improve such issues.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: k* Distribution: Evaluating the Latent Space of Deep Neural Networks using Local Neighborhood Analysis
Abstract: Most examinations of neural networks' learned latent spaces typically employ
dimensionality reduction techniques such as t-SNE or UMAP. While these methods
effectively capture the overall sample distribution in the entire learned
latent space, they tend to distort the structure of sample distributions within
specific classes in the subset of the latent space. This distortion complicates
the task of easily distinguishing classes identifiable by neural networks. In
response to this challenge, we introduce the k* Distribution methodology. This
approach focuses on capturing the characteristics and structure of sample
distributions for individual classes within the subset of the learned latent
space using local neighborhood analysis. The key concept is to facilitate easy
comparison of different k* distributions, enabling analysis of how various
classes are processed by the same neural network. This provides a more profound
understanding of existing contemporary visualizations. Our study reveals three
distinct distributions of samples within the learned latent space subset: a)
Fractured, b) Overlapped, and c) Clustered. We note and demonstrate that the
distribution of samples within the network's learned latent space significantly
varies depending on the class. Furthermore, we illustrate that our analysis can
be applied to explore the latent space of diverse neural network architectures,
various layers within neural networks, transformations applied to input
samples, and the distribution of training and testing data for neural networks.
We anticipate that our approach will facilitate more targeted investigations
into neural networks by collectively examining the distribution of different
samples within the learned latent space.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Coop: Memory is not a Commodity
Abstract: Tensor rematerialization allows the training of deep neural networks (DNNs)
under limited memory budgets by checkpointing the models and recomputing the
evicted tensors as needed. However, the existing tensor rematerialization
techniques overlook the memory system in deep learning frameworks and
implicitly assume that free memory blocks at different addresses are identical.
Under this flawed assumption, discontiguous tensors are evicted, among which
some are not used to allocate the new tensor. This leads to severe memory
fragmentation and increases the cost of potential rematerializations. To
address this issue, we propose to evict tensors within a sliding window to
ensure all evictions are contiguous and are immediately used. Furthermore, we
proposed cheap tensor partitioning and recomputable in-place to further reduce
the rematerialization cost by optimizing the tensor allocation. We named our
method Coop as it is a co-optimization of tensor allocation and tensor
rematerialization. We evaluated Coop on eight representative DNNs. The
experimental results demonstrate that Coop achieves up to $2\times$ memory
saving and hugely reduces compute overhead, search latency, and memory
fragmentation compared to the state-of-the-art baselines.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: EELBERT: Tiny Models through Dynamic Embeddings
Abstract: We introduce EELBERT, an approach for compression of transformer-based models
(e.g., BERT), with minimal impact on the accuracy of downstream tasks. This is
achieved by replacing the input embedding layer of the model with dynamic, i.e.
on-the-fly, embedding computations. Since the input embedding layer accounts
for a significant fraction of the model size, especially for the smaller BERT
variants, replacing this layer with an embedding computation function helps us
reduce the model size significantly. Empirical evaluation on the GLUE benchmark
shows that our BERT variants (EELBERT) suffer minimal regression compared to
the traditional BERT models. Through this approach, we are able to develop our
smallest model UNO-EELBERT, which achieves a GLUE score within 4% of fully
trained BERT-tiny, while being 15x smaller (1.2 MB) in size.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: A theory for the sparsity emerged in the Forward Forward algorithm
Abstract: This report explores the theory that explains the high sparsity phenomenon
\citep{tosato2023emergent} observed in the forward-forward algorithm
\citep{hinton2022forward}. The two theorems proposed predict the sparsity
changes of a single data point's activation in two cases: Theorem
\ref{theorem:1}: Decrease the goodness of the whole batch. Theorem
\ref{theorem:2}: Apply the complete forward forward algorithm to decrease the
goodness for negative data and increase the goodness for positive data. The
theory aligns well with the experiments tested on the MNIST dataset.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Modeling User Viewing Flow using Large Language Models for Article Recommendation
Abstract: This paper proposes the User Viewing Flow Modeling (SINGLE) method for the
article recommendation task, which models the user constant preference and
instant interest from user-clicked articles. Specifically, we employ a user
constant viewing flow modeling method to summarize the user's general interest
to recommend articles. We utilize Large Language Models (LLMs) to capture
constant user preferences from previously clicked articles, such as skills and
positions. Then we design the user instant viewing flow modeling method to
build interactions between user-clicked article history and candidate articles.
It attentively reads the representations of user-clicked articles and aims to
learn the user's different interest views to match the candidate article. Our
experimental results on the Alibaba Technology Association (ATA) website show
the advantage of SINGLE, which achieves 2.4% improvements over previous
baseline models in the online A/B test. Our further analyses illustrate that
SINGLE has the ability to build a more tailored recommendation system by
mimicking different article viewing behaviors of users and recommending more
appropriate and diverse articles to match user interests.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: FinanceBench: A New Benchmark for Financial Question Answering
Abstract: FinanceBench is a first-of-its-kind test suite for evaluating the performance
of LLMs on open book financial question answering (QA). It comprises 10,231
questions about publicly traded companies, with corresponding answers and
evidence strings. The questions in FinanceBench are ecologically valid and
cover a diverse set of scenarios. They are intended to be clear-cut and
straightforward to answer to serve as a minimum performance standard. We test
16 state of the art model configurations (including GPT-4-Turbo, Llama2 and
Claude2, with vector stores and long context prompts) on a sample of 150 cases
from FinanceBench, and manually review their answers (n=2,400). The cases are
available open-source. We show that existing LLMs have clear limitations for
financial QA. Notably, GPT-4-Turbo used with a retrieval system incorrectly
answered or refused to answer 81% of questions. While augmentation techniques
such as using longer context window to feed in relevant evidence improve
performance, they are unrealistic for enterprise settings due to increased
latency and cannot support larger financial documents. We find that all models
examined exhibit weaknesses, such as hallucinations, that limit their
suitability for use by enterprises.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Conflict Transformation and Management. From Cognitive Maps to Value Trees
Abstract: Conflict transformation and management are complex decision processes with
extremely high stakes at hand and could greatly benefit from formal approaches
to decision support. For this purpose we develop a general framework about how
to use problem structuring methods for such purposes. More precisely we show
how to transform cognitive maps to value trees in order to promote a more
design-oriented approach to decision support aiming at constructing innovative
solutions for conflict management purposes. We show that our findings have a
much wider validity since they allow to move from a descriptive representation
of a problem situation to a more prescriptive one using formal procedures and
models.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: IEKM: A Model Incorporating External Keyword Matrices
Abstract: A customer service platform system with a core text semantic similarity (STS)
task faces two urgent challenges: Firstly, one platform system needs to adapt
to different domains of customers, i.e., different domains adaptation (DDA).
Secondly, it is difficult for the model of the platform system to distinguish
sentence pairs that are literally close but semantically different, i.e., hard
negative samples. In this paper, we propose an incorporation external keywords
matrices model (IEKM) to address these challenges. The model uses external
tools or dictionaries to construct external matrices and fuses them to the
self-attention layers of the Transformer structure through gating units, thus
enabling flexible corrections to the model results. We evaluate the method on
multiple datasets and the results show that our method has improved performance
on all datasets. To demonstrate that our method can effectively solve all the
above challenges, we conduct a flexible correction experiment, which results in
an increase in the F1 value from 56.61 to 73.53. Our code will be publicly
available.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Data Management For Large Language Models: A Survey
Abstract: Data plays a fundamental role in the training of Large Language Models
(LLMs). Effective data management, particularly in the formulation of a
well-suited training dataset, holds significance for enhancing model
performance and improving training efficiency during pretraining and supervised
fine-tuning phases. Despite the considerable importance of data management, the
current research community still falls short in providing a systematic analysis
of the rationale behind management strategy selection, its consequential
effects, methodologies for evaluating curated datasets, and the ongoing pursuit
of improved strategies. Consequently, the exploration of data management has
attracted more and more attention among the research community. This survey
provides a comprehensive overview of current research in data management within
both the pretraining and supervised fine-tuning stages of LLMs, covering
various noteworthy aspects of data management strategy design: data quantity,
data quality, domain/task composition, etc. Looking toward the future, we
extrapolate existing challenges and outline promising directions for
development in this field. Therefore, this survey serves as a guiding resource
for practitioners aspiring to construct powerful LLMs through effective data
management practices. The collection of the latest papers is available at
https://github.com/ZigeW/data_management_LLM.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Resfusion: Prior Residual Noise embedded Denoising Diffusion Probabilistic Models
Abstract: Recently, Denoising Diffusion Probabilistic Models have been widely used in
image segmentation, by generating segmentation masks conditioned on the input
image. However, previous works can not seamlessly integrate existing end-to-end
models with denoising diffusion models. Existing research can only select
acceleration steps based on experience rather than calculating them
specifically. Moreover, most methods are limited to small models and
small-scale datasets, unable to generalize to general datasets and a wider
range of tasks. Therefore, we propose Resfusion with a novel resnoise-diffusion
process, which gradually generates segmentation masks or any type of target
image, seamlessly integrating state-of-the-art end-to-end models and denoising
diffusion models. Resfusion bridges the discrepancy between the likelihood
output and the ground truth output through a Markov process. Through the novel
smooth equivalence transformation in resnoise-diffusion process, we determine
the optimal acceleration step. Experimental results demonstrate that Resfusion
combines the capabilities of existing end-to-end models and denoising diffusion
models, further enhancing performance and achieving outstanding results.
Moreover, Resfusion is not limited to segmentation tasks, it can easily
generalize to any general tasks of image generation and exhibit strong
competitiveness.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: AI Alignment: A Comprehensive Survey
Abstract: AI alignment aims to make AI systems behave in line with human intentions and
values. As AI systems grow more capable, the potential large-scale risks
associated with misaligned AI systems become salient. Hundreds of AI experts
and public figures have expressed concerns about AI risks, arguing that
"mitigating the risk of extinction from AI should be a global priority,
alongside other societal-scale risks such as pandemics and nuclear war". To
provide a comprehensive and up-to-date overview of the alignment field, in this
survey paper, we delve into the core concepts, methodology, and practice of
alignment. We identify the RICE principles as the key objectives of AI
alignment: Robustness, Interpretability, Controllability, and Ethicality.
Guided by these four principles, we outline the landscape of current alignment
research and decompose them into two key components: forward alignment and
backward alignment. The former aims to make AI systems aligned via alignment
training, while the latter aims to gain evidence about the systems' alignment
and govern them appropriately to avoid exacerbating misalignment risks. Forward
alignment and backward alignment form a recurrent process where the alignment
of AI systems from the forward process is verified in the backward process,
meanwhile providing updated objectives for forward alignment in the next round.
On forward alignment, we discuss learning from feedback and learning under
distribution shift. On backward alignment, we discuss assurance techniques and
governance practices that apply to every stage of AI systems' lifecycle.
  We also release and continually update the website (www.alignmentsurvey.com)
which features tutorials, collections of papers, blog posts, and other
resources.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Solving MaxSAT with Matrix Multiplication
Abstract: We propose an incomplete algorithm for Maximum Satisfiability (MaxSAT)
specifically designed to run on neural network accelerators such as GPUs and
TPUs. Given a MaxSAT problem instance in conjunctive normal form, our procedure
constructs a Restricted Boltzmann Machine (RBM) with an equilibrium
distribution wherein the probability of a Boolean assignment is exponential in
the number of clauses it satisfies. Block Gibbs sampling is used to
stochastically search the space of assignments with parallel Markov chains.
Since matrix multiplication is the main computational primitive for block Gibbs
sampling in an RBM, our approach leads to an elegantly simple algorithm (40
lines of JAX) well-suited for neural network accelerators. Theoretical results
about RBMs guarantee that the required number of visible and hidden units of
the RBM scale only linearly with the number of variables and constant-sized
clauses in the MaxSAT instance, ensuring that the computational cost of a Gibbs
step scales reasonably with the instance size. Search throughput can be
increased by batching parallel chains within a single accelerator as well as by
distributing them across multiple accelerators. As a further enhancement, a
heuristic based on unit propagation running on CPU is periodically applied to
the sampled assignments. Our approach, which we term RbmSAT, is a new design
point in the algorithm-hardware co-design space for MaxSAT. We present timed
results on a subset of problem instances from the annual MaxSAT Evaluation's
Incomplete Unweighted Track for the years 2018 to 2021. When allotted the same
running time and CPU compute budget (but no TPUs), RbmSAT outperforms other
participating solvers on problems drawn from three out of the four years'
competitions. Given the same running time on a TPU cluster for which RbmSAT is
uniquely designed, it outperforms all solvers on problems drawn from all four
years.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Foundation Model Assisted Weakly Supervised Semantic Segmentation
Abstract: This work aims to leverage pre-trained foundation models, such as contrastive
language-image pre-training (CLIP) and segment anything model (SAM), to address
weakly supervised semantic segmentation (WSSS) using image-level labels. To
this end, we propose a coarse-to-fine framework based on CLIP and SAM for
generating high-quality segmentation seeds. Specifically, we construct an image
classification task and a seed segmentation task, which are jointly performed
by CLIP with frozen weights and two sets of learnable task-specific prompts. A
SAM-based seeding (SAMS) module is designed and applied to each task to produce
either coarse or fine seed maps. Moreover, we design a multi-label contrastive
loss supervised by image-level labels and a CAM activation loss supervised by
the generated coarse seed map. These losses are used to learn the prompts,
which are the only parts need to be learned in our framework. Once the prompts
are learned, we input each image along with the learned segmentation-specific
prompts into CLIP and the SAMS module to produce high-quality segmentation
seeds. These seeds serve as pseudo labels to train an off-the-shelf
segmentation network like other two-stage WSSS methods. Experiments show that
our method achieves the state-of-the-art performance on PASCAL VOC 2012 and
competitive results on MS COCO 2014. Code is available at
https://github.com/HAL-42/FMA-WSSS.git.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Case Repositories: Towards Case-Based Reasoning for AI Alignment
Abstract: Case studies commonly form the pedagogical backbone in law, ethics, and many
other domains that face complex and ambiguous societal questions informed by
human values. Similar complexities and ambiguities arise when we consider how
AI should be aligned in practice: when faced with vast quantities of diverse
(and sometimes conflicting) values from different individuals and communities,
with whose values is AI to align, and how should AI do so? We propose a
complementary approach to constitutional AI alignment, grounded in ideas from
case-based reasoning (CBR), that focuses on the construction of policies
through judgments on a set of cases. We present a process to assemble such a
case repository by: 1) gathering a set of ``seed'' cases -- questions one may
ask an AI system -- in a particular domain, 2) eliciting domain-specific key
dimensions for cases through workshops with domain experts, 3) using LLMs to
generate variations of cases not seen in the wild, and 4) engaging with the
public to judge and improve cases. We then discuss how such a case repository
could assist in AI alignment, both through directly acting as precedents to
ground acceptable behaviors, and as a medium for individuals and communities to
engage in moral reasoning around AI.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Towards Effective Paraphrasing for Information Disguise
Abstract: Information Disguise (ID), a part of computational ethics in Natural Language
Processing (NLP), is concerned with best practices of textual paraphrasing to
prevent the non-consensual use of authors' posts on the Internet. Research on
ID becomes important when authors' written online communication pertains to
sensitive domains, e.g., mental health. Over time, researchers have utilized
AI-based automated word spinners (e.g., SpinRewriter, WordAI) for paraphrasing
content. However, these tools fail to satisfy the purpose of ID as their
paraphrased content still leads to the source when queried on search engines.
There is limited prior work on judging the effectiveness of paraphrasing
methods for ID on search engines or their proxies, neural retriever (NeurIR)
models. We propose a framework where, for a given sentence from an author's
post, we perform iterative perturbation on the sentence in the direction of
paraphrasing with an attempt to confuse the search mechanism of a NeurIR system
when the sentence is queried on it. Our experiments involve the subreddit
'r/AmItheAsshole' as the source of public content and Dense Passage Retriever
as a NeurIR system-based proxy for search engines. Our work introduces a novel
method of phrase-importance rankings using perplexity scores and involves
multi-level phrase substitutions via beam search. Our multi-phrase substitution
scheme succeeds in disguising sentences 82% of the time and hence takes an
essential step towards enabling researchers to disguise sensitive content
effectively before making it public. We also release the code of our approach.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Data Valuation and Detections in Federated Learning
Abstract: Federated Learning (FL) enables collaborative model training while preserving
the privacy of raw data. A challenge in this framework is the fair and
efficient valuation of data, which is crucial for incentivizing clients to
contribute high-quality data in the FL task. In scenarios involving numerous
data clients within FL, it is often the case that only a subset of clients and
datasets are pertinent to a specific learning task, while others might have
either a negative or negligible impact on the model training process. This
paper introduces a novel privacy-preserving method for evaluating client
contributions and selecting relevant datasets without a pre-specified training
algorithm in an FL task. Our proposed approach FedBary, utilizes Wasserstein
distance within the federated context, offering a new solution for data
valuation in the FL framework. This method ensures transparent data valuation
and efficient computation of the Wasserstein barycenter and reduces the
dependence on validation datasets. Through extensive empirical experiments and
theoretical analyses, we demonstrate the potential of this data valuation
method as a promising avenue for FL research.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks
Abstract: Recent efforts in natural language processing (NLP) commonsense reasoning
research have yielded a considerable number of new datasets and benchmarks.
However, most of these datasets formulate commonsense reasoning challenges in
artificial scenarios that are not reflective of the tasks which real-world NLP
systems are designed to solve. In this work, we present CRoW, a
manually-curated, multi-task benchmark that evaluates the ability of models to
apply commonsense reasoning in the context of six real-world NLP tasks. CRoW is
constructed using a multi-stage data collection pipeline that rewrites examples
from existing datasets using commonsense-violating perturbations. We use CRoW
to study how NLP systems perform across different dimensions of commonsense
knowledge, such as physical, temporal, and social reasoning. We find a
significant performance gap when NLP systems are evaluated on CRoW compared to
humans, showcasing that commonsense reasoning is far from being solved in
real-world task settings. We make our dataset and leaderboard available to the
research community at https://github.com/mismayil/crow.

Output: Computational Linguistics