Instruction: What field is the article from?

Input:
Title: KOALA: Self-Attention Matters in Knowledge Distillation of Latent Diffusion Models for Memory-Efficient and Fast Image Synthesis
Abstract: Stable diffusion is the mainstay of the text-to-image (T2I) synthesis in the
community due to its generation performance and open-source nature. Recently,
Stable Diffusion XL (SDXL), the successor of stable diffusion, has received a
lot of attention due to its significant performance improvements with a higher
resolution of 1024x1024 and a larger model. However, its increased computation
cost and model size require higher-end hardware(e.g., bigger VRAM GPU) for
end-users, incurring higher costs of operation. To address this problem, in
this work, we propose an efficient latent diffusion model for text-to-image
synthesis obtained by distilling the knowledge of SDXL. To this end, we first
perform an in-depth analysis of the denoising U-Net in SDXL, which is the main
bottleneck of the model, and then design a more efficient U-Net based on the
analysis. Secondly, we explore how to effectively distill the generation
capability of SDXL into an efficient U-Net and eventually identify four
essential factors, the core of which is that self-attention is the most
important part. With our efficient U-Net and self-attention-based knowledge
distillation strategy, we build our efficient T2I models, called KOALA-1B &
-700M, while reducing the model size up to 54% and 69% of the original SDXL
model. In particular, the KOALA-700M is more than twice as fast as SDXL while
still retaining a decent generation quality. We hope that due to its balanced
speed-performance tradeoff, our KOALA models can serve as a cost-effective
alternative to SDXL in resource-constrained environments.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Aligning with Whom? Large Language Models Have Gender and Racial Biases in Subjective NLP Tasks
Abstract: Human perception of language depends on personal backgrounds like gender and
ethnicity. While existing studies have shown that large language models (LLMs)
hold values that are closer to certain societal groups, it is unclear whether
their prediction behaviors on subjective NLP tasks also exhibit a similar bias.
In this study, leveraging the POPQUORN dataset which contains annotations of
diverse demographic backgrounds, we conduct a series of experiments on four
popular LLMs to investigate their capability to understand group differences
and potential biases in their predictions for politeness and offensiveness. We
find that for both tasks, model predictions are closer to the labels from White
and female participants. We further explore prompting with the target
demographic labels and show that including the target demographic in the prompt
actually worsens the model's performance. More specifically, when being
prompted to respond from the perspective of "Black" and "Asian" individuals,
models show lower performance in predicting both overall scores as well as the
scores from corresponding groups. Our results suggest that LLMs hold gender and
racial biases for subjective NLP tasks and that demographic-infused prompts
alone may be insufficient to mitigate such effects. Code and data are available
at https://github.com/Jiaxin-Pei/LLM-Group-Bias.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Web News Timeline Generation with Extended Task Prompting
Abstract: The creation of news timeline is essential for a comprehensive and contextual
understanding of events as they unfold over time. This approach aids in
discerning patterns and trends that might be obscured when news is viewed in
isolation. By organizing news in a chronological sequence, it becomes easier to
track the development of stories, understand the interrelation of events, and
grasp the broader implications of news items. This is particularly helpful in
sectors like finance and insurance, where timely understanding of the event
development-ranging from extreme weather to political upheavals and health
crises-is indispensable for effective risk management. While traditional
natural language processing (NLP) techniques have had some success, they often
fail to capture the news with nuanced relevance that are readily apparent to
domain experts, hindering broader industry integration. The advance of Large
Language Models (LLMs) offers a renewed opportunity to tackle this challenge.
However, direct prompting LLMs for this task is often ineffective. Our study
investigates the application of an extended task prompting technique to assess
past news relevance. We demonstrate that enhancing conventional prompts with
additional tasks boosts their effectiveness on various news dataset, rendering
news timeline generation practical for professional use. This work has been
deployed as a publicly accessible browser extension which is adopted within our
network.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models
Abstract: Nature evolves creatures with a high complexity of morphological and
behavioral intelligence, meanwhile computational methods lag in approaching
that diversity and efficacy. Co-optimization of artificial creatures'
morphology and control in silico shows promise for applications in physical
soft robotics and virtual character creation; such approaches, however, require
developing new learning algorithms that can reason about function atop pure
structure. In this paper, we present DiffuseBot, a physics-augmented diffusion
model that generates soft robot morphologies capable of excelling in a wide
spectrum of tasks. DiffuseBot bridges the gap between virtually generated
content and physical utility by (i) augmenting the diffusion process with a
physical dynamical simulation which provides a certificate of performance, and
(ii) introducing a co-design procedure that jointly optimizes physical design
and control by leveraging information about physical sensitivities from
differentiable simulation. We showcase a range of simulated and fabricated
robots along with their capabilities. Check our website at
https://diffusebot.github.io/

Output: Robotics


Instruction: What field is the article from?

Input:
Title: In Search of Lost Online Test-time Adaptation: A Survey
Abstract: In this paper, we present a comprehensive survey on online test-time
adaptation (OTTA), a paradigm focused on adapting machine learning models to
novel data distributions upon batch arrival. Despite the proliferation of OTTA
methods recently, the field is mired in issues like ambiguous settings,
antiquated backbones, and inconsistent hyperparameter tuning, obfuscating the
real challenges and making reproducibility elusive. For clarity and a rigorous
comparison, we classify OTTA techniques into three primary categories and
subject them to benchmarks using the potent Vision Transformer (ViT) backbone
to discover genuinely effective strategies. Our benchmarks span not only
conventional corrupted datasets such as CIFAR-10/100-C and ImageNet-C but also
real-world shifts embodied in CIFAR-10.1 and CIFAR-10-Warehouse, encapsulating
variations across search engines and synthesized data by diffusion models. To
gauge efficiency in online scenarios, we introduce novel evaluation metrics,
inclusive of FLOPs, shedding light on the trade-offs between adaptation
accuracy and computational overhead. Our findings diverge from existing
literature, indicating: (1) transformers exhibit heightened resilience to
diverse domain shifts, (2) the efficacy of many OTTA methods hinges on ample
batch sizes, and (3) stability in optimization and resistance to perturbations
are critical during adaptation, especially when the batch size is 1. Motivated
by these insights, we pointed out promising directions for future research. The
source code will be made available.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Improving Faithfulness for Vision Transformers
Abstract: Vision Transformers (ViTs) have achieved state-of-the-art performance for
various vision tasks. One reason behind the success lies in their ability to
provide plausible innate explanations for the behavior of neural architectures.
However, ViTs suffer from issues with explanation faithfulness, as their focal
points are fragile to adversarial attacks and can be easily changed with even
slight perturbations on the input image. In this paper, we propose a rigorous
approach to mitigate these issues by introducing Faithful ViTs (FViTs). Briefly
speaking, an FViT should have the following two properties: (1) The top-$k$
indices of its self-attention vector should remain mostly unchanged under input
perturbation, indicating stable explanations; (2) The prediction distribution
should be robust to perturbations. To achieve this, we propose a new method
called Denoised Diffusion Smoothing (DDS), which adopts randomized smoothing
and diffusion-based denoising. We theoretically prove that processing ViTs
directly with DDS can turn them into FViTs. We also show that Gaussian noise is
nearly optimal for both $\ell_2$ and $\ell_\infty$-norm cases. Finally, we
demonstrate the effectiveness of our approach through comprehensive experiments
and evaluations. Specifically, we compare our FViTs with other baselines
through visual interpretation and robustness accuracy under adversarial
attacks. Results show that FViTs are more robust against adversarial attacks
while maintaining the explainability of attention, indicating higher
faithfulness.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: 4M: Massively Multimodal Masked Modeling
Abstract: Current machine learning models for vision are often highly specialized and
limited to a single modality and task. In contrast, recent large language
models exhibit a wide range of capabilities, hinting at a possibility for
similarly versatile models in computer vision. In this paper, we take a step in
this direction and propose a multimodal training scheme called 4M. It consists
of training a single unified Transformer encoder-decoder using a masked
modeling objective across a wide range of input/output modalities - including
text, images, geometric, and semantic modalities, as well as neural network
feature maps. 4M achieves scalability by unifying the representation space of
all modalities through mapping them into discrete tokens and performing
multimodal masked modeling on a small randomized subset of tokens.
  4M leads to models that exhibit several key capabilities: (1) they can
perform a diverse set of vision tasks out of the box, (2) they excel when
fine-tuned for unseen downstream tasks or new input modalities, and (3) they
can function as a generative model that can be conditioned on arbitrary
modalities, enabling a wide variety of expressive multimodal editing
capabilities with remarkable flexibility.
  Through experimental analyses, we demonstrate the potential of 4M for
training versatile and scalable foundation models for vision tasks, setting the
stage for further exploration in multimodal learning for vision and other
domains.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Adaptive Image Registration: A Hybrid Approach Integrating Deep Learning and Optimization Functions for Enhanced Precision
Abstract: Image registration has traditionally been done using two distinct approaches:
learning based methods, relying on robust deep neural networks, and
optimization-based methods, applying complex mathematical transformations to
warp images accordingly. Of course, both paradigms offer advantages and
disadvantages, and, in this work, we seek to combine their respective strengths
into a single streamlined framework, using the outputs of the learning based
method as initial parameters for optimization while prioritizing computational
power for the image pairs that offer the greatest loss. Our investigations
showed that an improvement of 1.5% in testing when utilizing the best
performing state-of-the-art model as the backbone of the framework, while
maintaining the same inference time and a substantial 0.94% points performance
gain in deformation field smoothness.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: A Social-aware Gaussian Pre-trained Model for Effective Cold-start Recommendation
Abstract: The use of pre-training is an emerging technique to enhance a neural model's
performance, which has been shown to be effective for many neural language
models such as BERT. This technique has also been used to enhance the
performance of recommender systems. In such recommender systems, pre-training
models are used to learn a better initialisation for both users and items.
However, recent existing pre-trained recommender systems tend to only
incorporate the user interaction data at the pre-training stage, making it
difficult to deliver good recommendations, especially when the interaction data
is sparse. To alleviate this common data sparsity issue, we propose to
pre-train the recommendation model not only with the interaction data but also
with other available information such as the social relations among users,
thereby providing the recommender system with a better initialisation compared
with solely relying on the user interaction data. We propose a novel
recommendation model, the Social-aware Gaussian Pre-trained model (SGP), which
encodes the user social relations and interaction data at the pre-training
stage in a Graph Neural Network (GNN). Afterwards, in the subsequent
fine-tuning stage, our SGP model adopts a Gaussian Mixture Model (GMM) to
factorise these pre-trained embeddings for further training, thereby benefiting
the cold-start users from these pre-built social relations. Our extensive
experiments on three public datasets show that, in comparison to 16 competitive
baselines, our SGP model significantly outperforms the best baseline by upto
7.7% in terms of NDCG@10. In addition, we show that SGP permits to effectively
alleviate the cold-start problem, especially when users newly register to the
system through their friends' suggestions.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Teaching Specific Scientific Knowledge into Large Language Models through Additional Training
Abstract: Through additional training, we explore embedding specialized scientific
knowledge into the Llama 2 Large Language Model (LLM). Key findings reveal that
effective knowledge integration requires reading texts from multiple
perspectives, especially in instructional formats. We utilize text augmentation
to tackle the scarcity of specialized texts, including style conversions and
translations. Hyperparameter optimization proves crucial, with different size
models (7b, 13b, and 70b) reasonably undergoing additional training. Validating
our methods, we construct a dataset of 65,000 scientific papers. Although we
have succeeded in partially embedding knowledge, the study highlights the
complexities and limitations of incorporating specialized information into
LLMs, suggesting areas for further improvement.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Combinatorial Optimization with Policy Adaptation using Latent Space Search
Abstract: Combinatorial Optimization underpins many real-world applications and yet,
designing performant algorithms to solve these complex, typically NP-hard,
problems remains a significant research challenge. Reinforcement Learning (RL)
provides a versatile framework for designing heuristics across a broad spectrum
of problem domains. However, despite notable progress, RL has not yet
supplanted industrial solvers as the go-to solution. Current approaches
emphasize pre-training heuristics that construct solutions but often rely on
search procedures with limited variance, such as stochastically sampling
numerous solutions from a single policy or employing computationally expensive
fine-tuning of the policy on individual problem instances. Building on the
intuition that performant search at inference time should be anticipated during
pre-training, we propose COMPASS, a novel RL approach that parameterizes a
distribution of diverse and specialized policies conditioned on a continuous
latent space. We evaluate COMPASS across three canonical problems - Travelling
Salesman, Capacitated Vehicle Routing, and Job-Shop Scheduling - and
demonstrate that our search strategy (i) outperforms state-of-the-art
approaches on 11 standard benchmarking tasks and (ii) generalizes better,
surpassing all other approaches on a set of 18 procedurally transformed
instance distributions.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion
Abstract: Knowledge graphs generally suffer from incompleteness, which can be
alleviated by completing the missing information. Deep knowledge convolutional
embedding models based on neural networks are currently popular methods for
knowledge graph completion. However, most existing methods use external
convolution kernels and traditional plain convolution processes, which limits
the feature interaction capability of the model. In this paper, we propose a
novel dynamic convolutional embedding model ConvD for knowledge graph
completion, which directly reshapes the relation embeddings into multiple
internal convolution kernels to improve the external convolution kernels of the
traditional convolutional embedding model. The internal convolution kernels can
effectively augment the feature interaction between the relation embeddings and
entity embeddings, thus enhancing the model embedding performance. Moreover, we
design a priori knowledge-optimized attention mechanism, which can assign
different contribution weight coefficients to multiple relation convolution
kernels for dynamic convolution to improve the expressiveness of the model
further. Extensive experiments on various datasets show that our proposed model
consistently outperforms the state-of-the-art baseline methods, with average
improvements ranging from 11.30\% to 16.92\% across all model evaluation
metrics. Ablation experiments verify the effectiveness of each component module
of the ConvD model.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching
Abstract: The lightweight "local-match-global" matching introduced by SRe2L
successfully creates a distilled dataset with comprehensive information on the
full 224x224 ImageNet-1k. However, this one-sided approach is limited to a
particular backbone, layer, and statistics, which limits the improvement of the
generalization of a distilled dataset. We suggest that sufficient and various
"local-match-global" matching are more precise and effective than a single one
and has the ability to create a distilled dataset with richer information and
better generalization. We call this perspective "generalized matching" and
propose Generalized Various Backbone and Statistical Matching (G-VBSM) in this
work, which aims to create a synthetic dataset with densities, ensuring
consistency with the complete dataset across various backbones, layers, and
statistics. As experimentally demonstrated, G-VBSM is the first algorithm to
obtain strong performance across both small-scale and large-scale datasets.
Specifically, G-VBSM achieves a performance of 38.7% on CIFAR-100 with
128-width ConvNet, 47.6% on Tiny-ImageNet with ResNet18, and 31.4% on the full
224x224 ImageNet-1k with ResNet18, under images per class (IPC) 10, 50, and 10,
respectively. These results surpass all SOTA methods by margins of 3.9%, 6.5%,
and 10.1%, respectively.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: VRPTEST: Evaluating Visual Referring Prompting in Large Multimodal Models
Abstract: With recent advancements in Large Multimodal Models (LMMs) across various
domains, a novel prompting method called visual referring prompting has
emerged, showing significant potential in enhancing human-computer interaction
within multimodal systems. This method offers a more natural and flexible
approach to human interaction with these systems compared to traditional text
descriptions or coordinates. However, the categorization of visual referring
prompting remains undefined, and its impact on the performance of LMMs has yet
to be formally examined. In this study, we conduct the first comprehensive
analysis of LMMs using a variety of visual referring prompting strategies. We
introduce a benchmark dataset called VRPTEST, comprising 3 different visual
tasks and 2,275 images, spanning diverse combinations of prompt strategies.
Using VRPTEST, we conduct a comprehensive evaluation of eight versions of
prominent open-source and proprietary foundation models, including two early
versions of GPT-4V. We develop an automated assessment framework based on
software metamorphic testing techniques to evaluate the accuracy of LMMs
without the need for human intervention or manual labeling. We find that the
current proprietary models generally outperform the open-source ones, showing
an average accuracy improvement of 22.70%; however, there is still potential
for improvement. Moreover, our quantitative analysis shows that the choice of
prompt strategy significantly affects the accuracy of LMMs, with variations
ranging from -17.5% to +7.3%. Further case studies indicate that an appropriate
visual referring prompting strategy can improve LMMs' understanding of context
and location information, while an unsuitable one might lead to answer
rejection. We also provide insights on minimizing the negative impact of visual
referring prompting on LMMs.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering
Abstract: Large language models (LLMs) demonstrate emergent in-context learning
capabilities, where they adapt to new tasks based on example demonstrations.
However, in-context learning has seen limited effectiveness in many settings,
is difficult to quantitatively control and takes up context window space. To
overcome these limitations, we propose an alternative approach that recasts
in-context learning as in-context vectors (ICV). Using ICV has two steps. We
first use a forward pass on demonstration examples to create the in-context
vector from the latent embedding of the LLM. This vector captures essential
information about the intended task. On a new query, instead of adding
demonstrations to the prompt, we shift the latent states of the LLM using the
ICV. The ICV approach has several benefits: 1) it enables the LLM to more
effectively follow the demonstration examples; 2) it's easy to control by
adjusting the magnitude of the ICV; 3) it reduces the length of the prompt by
removing the in-context demonstrations; 4) ICV is computationally much more
efficient than fine-tuning. We demonstrate that ICV achieves better performance
compared to standard in-context learning and fine-tuning on diverse tasks
including safety, style transfer, role-playing and formatting. Moreover, we
show that we can flexibly teach LLM to simultaneously follow different types of
instructions by simple vector arithmetics on the corresponding ICVs.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: PINNs-Based Uncertainty Quantification for Transient Stability Analysis
Abstract: This paper addresses the challenge of transient stability in power systems
with missing parameters and uncertainty propagation in swing equations. We
introduce a novel application of Physics-Informed Neural Networks (PINNs),
specifically an Ensemble of PINNs (E-PINNs), to estimate critical parameters
like rotor angle and inertia coefficient with enhanced accuracy and reduced
computational load. E-PINNs capitalize on the underlying physical principles of
swing equations to provide a robust solution. Our approach not only facilitates
efficient parameter estimation but also quantifies uncertainties, delivering
probabilistic insights into the system behavior. The efficacy of E-PINNs is
demonstrated through the analysis of $1$-bus and $2$-bus systems, highlighting
the model's ability to handle parameter variability and data scarcity. The
study advances the application of machine learning in power system stability,
paving the way for reliable and computationally efficient transient stability
analysis.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection
Abstract: Detecting anomalies in real-world multivariate time series data is
challenging due to complex temporal dependencies and inter-variable
correlations. Recently, reconstruction-based deep models have been widely used
to solve the problem. However, these methods still suffer from an
over-generalization issue and fail to deliver consistently high performance. To
address this issue, we propose the MEMTO, a memory-guided Transformer using a
reconstruction-based approach. It is designed to incorporate a novel memory
module that can learn the degree to which each memory item should be updated in
response to the input data. To stabilize the training procedure, we use a
two-phase training paradigm which involves using K-means clustering for
initializing memory items. Additionally, we introduce a bi-dimensional
deviation-based detection criterion that calculates anomaly scores considering
both input space and latent space. We evaluate our proposed method on five
real-world datasets from diverse domains, and it achieves an average anomaly
detection F1-score of 95.74%, significantly outperforming the previous
state-of-the-art methods. We also conduct extensive experiments to empirically
validate the effectiveness of our proposed model's key components.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Effectively Fine-tune to Improve Large Multimodal Models for Radiology Report Generation
Abstract: Writing radiology reports from medical images requires a high level of domain
expertise. It is time-consuming even for trained radiologists and can be
error-prone for inexperienced radiologists. It would be appealing to automate
this task by leveraging generative AI, which has shown drastic progress in
vision and language understanding. In particular, Large Language Models (LLM)
have demonstrated impressive capabilities recently and continued to set new
state-of-the-art performance on almost all natural language tasks. While many
have proposed architectures to combine vision models with LLMs for multimodal
tasks, few have explored practical fine-tuning strategies. In this work, we
proposed a simple yet effective two-stage fine-tuning protocol to align visual
features to LLM's text embedding space as soft visual prompts. Our framework
with OpenLLaMA-7B achieved state-of-the-art level performance without
domain-specific pretraining. Moreover, we provide detailed analyses of soft
visual prompts and attention mechanisms, shedding light on future research
directions.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Automating the Correctness Assessment of AI-generated Code for Security Contexts
Abstract: In this paper, we propose a fully automated method, named ACCA, to evaluate
the correctness of AI-generated code for security purposes. The method uses
symbolic execution to assess whether the AI-generated code behaves as a
reference implementation. We use ACCA to assess four state-of-the-art models
trained to generate security-oriented assembly code and compare the results of
the evaluation with different baseline solutions, including output similarity
metrics, widely used in the field, and the well-known ChatGPT, the AI-powered
language model developed by OpenAI. Our experiments show that our method
outperforms the baseline solutions and assesses the correctness of the
AI-generated code similar to the human-based evaluation, which is considered
the ground truth for the assessment in the field. Moreover, ACCA has a very
strong correlation with human evaluation (Pearson's correlation coefficient
r=0.84 on average). Finally, since it is a fully automated solution that does
not require any human intervention, the proposed method performs the assessment
of every code snippet in ~0.17s on average, which is definitely lower than the
average time required by human analysts to manually inspect the code, based on
our experience.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Human Conditional Reasoning in Answer Set Programming
Abstract: Given a conditional sentence P=>Q (if P then Q) and respective facts, four
different types of inferences are observed in human reasoning. Affirming the
antecedent (AA) (or modus ponens) reasons Q from P; affirming the consequent
(AC) reasons P from Q; denying the antecedent (DA) reasons -Q from -P; and
denying the consequent (DC) (or modus tollens) reasons -P from -Q. Among them,
AA and DC are logically valid, while AC and DA are logically invalid and often
called logical fallacies. Nevertheless, humans often perform AC or DA as
pragmatic inference in daily life. In this paper, we realize AC, DA and DC
inferences in answer set programming. Eight different types of completion are
introduced and their semantics are given by answer sets. We investigate formal
properties and characterize human reasoning tasks in cognitive psychology.
Those completions are also applied to commonsense reasoning in AI.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Which AI Technique Is Better to Classify Requirements? An Experiment with SVM, LSTM, and ChatGPT
Abstract: Context and motivation: Recently, Large Language Models (LLMs) like ChatGPT
have demonstrated remarkable proficiency in various Natural Language Processing
(NLP) tasks. Their application in Requirements Engineering (RE), especially in
requirements classification, has gained increasing interest. Question/problem:
In our research, we conducted an extensive empirical evaluation of ChatGPT
models including text-davinci-003, gpt-3.5-turbo, and gpt-4 in both zero-shot
and few-shot settings for requirements classification. The question arises as
to how these models compare to traditional classification methods, specifically
Support Vector Machine (SVM) and Long Short-Term Memory (LSTM). Principal
ideas/results: Based on five diverse datasets, our results show that ChatGPT
consistently outperforms LSTM, and while ChatGPT is more effective than SVM in
classifying functional requirements (FR), SVM is better in classifying
non-functional requirements (NFR). Our results also show that contrary to our
expectations, the few-shot setting does not always lead to enhanced
performance; in most instances, it was found to be suboptimal. Contribution:
Our findings underscore the potential of LLMs in the RE domain, suggesting that
they could play a pivotal role in future software engineering processes,
particularly as tools to enhance requirements classification.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Large Language Models in Law: A Survey
Abstract: The advent of artificial intelligence (AI) has significantly impacted the
traditional judicial industry. Moreover, recently, with the development of
AI-generated content (AIGC), AI and law have found applications in various
domains, including image recognition, automatic text generation, and
interactive chat. With the rapid emergence and growing popularity of large
models, it is evident that AI will drive transformation in the traditional
judicial industry. However, the application of legal large language models
(LLMs) is still in its nascent stage. Several challenges need to be addressed.
In this paper, we aim to provide a comprehensive survey of legal LLMs. We not
only conduct an extensive survey of LLMs, but also expose their applications in
the judicial system. We first provide an overview of AI technologies in the
legal field and showcase the recent research in LLMs. Then, we discuss the
practical implementation presented by legal LLMs, such as providing legal
advice to users and assisting judges during trials. In addition, we explore the
limitations of legal LLMs, including data, algorithms, and judicial practice.
Finally, we summarize practical recommendations and propose future development
directions to address these challenges.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: MACE: A Multi-pattern Accommodated and Efficient Anomaly Detection Method in the Frequency Domain
Abstract: Anomaly detection significantly enhances the robustness of cloud systems.
While neural network-based methods have recently demonstrated strong
advantages, they encounter practical challenges in cloud environments: the
contradiction between the impracticality of maintaining a unique model for each
service and the limited ability of dealing with diverse normal patterns by a
unified model, as well as issues with handling heavy traffic in real time and
short-term anomaly detection sensitivity. Thus, we propose MACE, a
Multi-pattern Accommodated and efficient Anomaly detection method in the
frequency domain for time series anomaly detection. There are three novel
characteristics of it: (i) a pattern extraction mechanism excelling at handling
diverse normal patterns, which enables the model to identify anomalies by
examining the correlation between the data sample and its service normal
pattern, instead of solely focusing on the data sample itself; (ii) a dualistic
convolution mechanism that amplifies short-term anomalies in the time domain
and hinders the reconstruction of anomalies in the frequency domain, which
enlarges the reconstruction error disparity between anomaly and normality and
facilitates anomaly detection; (iii) leveraging the sparsity and parallelism of
frequency domain to enhance model efficiency. We theoretically and
experimentally prove that using a strategically selected subset of Fourier
bases can not only reduce computational overhead but is also profit to
distinguish anomalies, compared to using the complete spectrum. Moreover,
extensive experiments demonstrate MACE's effectiveness in handling diverse
normal patterns with a unified model and it achieves state-of-the-art
performance with high efficiency. \end{abstract}

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: MGAS: Multi-Granularity Architecture Search for Trade-Off Between Model Effectiveness and Efficiency
Abstract: Neural architecture search (NAS) has gained significant traction in
automating the design of neural networks. To reduce the time cost,
differentiable architecture search (DAS) transforms the traditional paradigm of
discrete candidate sampling and evaluation into that of differentiable
super-net optimization and discretization. However, existing DAS methods fail
to trade off between model performance and model size. They either only conduct
coarse-grained operation-level search, which results in redundant model
parameters, or restrictively explore fine-grained filter-level and weight-level
units with pre-defined remaining ratios, suffering from excessive pruning
problem. Additionally, these methods compromise search quality to save memory
during the search process. To tackle these issues, we introduce
multi-granularity architecture search (MGAS), a unified framework which aims to
discover both effective and efficient neural networks by comprehensively yet
memory-efficiently exploring the multi-granularity search space. Specifically,
we improve the existing DAS methods in two aspects. First, we balance the model
unit numbers at different granularity levels with adaptive pruning. We learn
discretization functions specific to each granularity level to adaptively
determine the unit remaining ratio according to the evolving architecture.
Second, we reduce the memory consumption without degrading the search quality
using multi-stage search. We break down the super-net optimization and
discretization into multiple sub-net stages, and perform progressive
re-evaluation to allow for re-pruning and regrowing of previous units during
subsequent stages, compensating for potential bias. Extensive experiments on
CIFAR-10, CIFAR-100 and ImageNet demonstrate that MGAS outperforms other
state-of-the-art methods in achieving a better trade-off between model
performance and model size.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Exploring Data Augmentations on Self-/Semi-/Fully- Supervised Pre-trained Models
Abstract: Data augmentation has become a standard component of vision pre-trained
models to capture the invariance between augmented views. In practice,
augmentation techniques that mask regions of a sample with zero/mean values or
patches from other samples are commonly employed in pre-trained models with
self-/semi-/fully-supervised contrastive losses. However, the underlying
mechanism behind the effectiveness of these augmentation techniques remains
poorly explored. To investigate the problems, we conduct an empirical study to
quantify how data augmentation affects performance. Concretely, we apply 4
types of data augmentations termed with Random Erasing, CutOut, CutMix and
MixUp to a series of self-/semi-/fully- supervised pre-trained models. We
report their performance on vision tasks such as image classification, object
detection, instance segmentation, and semantic segmentation. We then explicitly
evaluate the invariance and diversity of the feature embedding. We observe
that: 1) Masking regions of the images decreases the invariance of the learned
feature embedding while providing a more considerable diversity. 2) Manual
annotations do not change the invariance or diversity of the learned feature
embedding. 3) The MixUp approach improves the diversity significantly, with
only a marginal decrease in terms of the invariance.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model
Abstract: In the realm of language models, the nuanced linguistic and cultural
intricacies of Traditional Chinese, as spoken in Taiwan, have been largely
overlooked. This paper introduces Taiwan LLM, a pioneering Large Language Model
that specifically caters to the Traditional Chinese language, with a focus on
the variant used in Taiwan. Leveraging a comprehensive pretraining corpus and
instruction-finetuning datasets, we have developed a model that not only
understands the complexities of Traditional Chinese but also embodies the
cultural context of Taiwan. Taiwan LLM represents the first of its kind, a
model that is not only linguistically accurate but also culturally resonant
with its user base. Our evaluations demonstrate that Taiwan LLM achieves
superior performance in understanding and generating Traditional Chinese text,
outperforming existing models that are predominantly trained on Simplified
Chinese or English. The open-source release of Taiwan LLM invites collaboration
and further innovation, ensuring that the linguistic diversity of Chinese
speakers is embraced and well-served. The model, datasets, and further
resources are made publicly available to foster ongoing research and
development in this field.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Rare Event Probability Learning by Normalizing Flows
Abstract: A rare event is defined by a low probability of occurrence. Accurate
estimation of such small probabilities is of utmost importance across diverse
domains. Conventional Monte Carlo methods are inefficient, demanding an
exorbitant number of samples to achieve reliable estimates. Inspired by the
exact sampling capabilities of normalizing flows, we revisit this challenge and
propose normalizing flow assisted importance sampling, termed NOFIS. NOFIS
first learns a sequence of proposal distributions associated with predefined
nested subset events by minimizing KL divergence losses. Next, it estimates the
rare event probability by utilizing importance sampling in conjunction with the
last proposal. The efficacy of our NOFIS method is substantiated through
comprehensive qualitative visualizations, affirming the optimality of the
learned proposal distribution, as well as a series of quantitative experiments
encompassing $10$ distinct test cases, which highlight NOFIS's superiority over
baseline approaches.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment
Abstract: Driving is a complex daily activity indicating age and disease related
cognitive declines. Therefore, deficits in driving performance compared with
ones without mild cognitive impairment (MCI) can reflect changes in cognitive
functioning. There is increasing evidence that unobtrusive monitoring of older
adults driving performance in a daily-life setting may allow us to detect
subtle early changes in cognition. The objectives of this paper include
designing low-cost in-vehicle sensing hardware capable of obtaining
high-precision positioning and telematics data, identifying important
indicators for early changes in cognition, and detecting early-warning signs of
cognitive impairment in a truly normal, day-to-day driving condition with
machine learning approaches. Our statistical analysis comparing drivers with
MCI to those without reveals that those with MCI exhibit smoother and safer
driving patterns. This suggests that drivers with MCI are cognizant of their
condition and tend to avoid erratic driving behaviors. Furthermore, our Random
Forest models identified the number of night trips, number of trips, and
education as the most influential factors in our data evaluation.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Graph Pre-training and Prompt Learning for Recommendation
Abstract: GNN-based recommenders have excelled in modeling intricate user-item
interactions through multi-hop message passing. However, existing methods often
overlook the dynamic nature of evolving user-item interactions, which impedes
the adaption to changing user preferences and distribution shifts in newly
arriving data. Thus, their scalability and performances in real-world dynamic
environments are limited. In this study, we propose GraphPL, a framework that
incorporates parameter-efficient and dynamic graph pre-training with prompt
learning. This novel combination empowers GNNs to effectively capture both
long-term user preferences and short-term behavior dynamics, enabling the
delivery of accurate and timely recommendations. Our GraphPL framework
addresses the challenge of evolving user preferences by seamlessly integrating
a temporal prompt mechanism and a graph-structural prompt learning mechanism
into the pre-trained GNN model. The temporal prompt mechanism encodes time
information on user-item interaction, allowing the model to naturally capture
temporal context, while the graph-structural prompt learning mechanism enables
the transfer of pre-trained knowledge to adapt to behavior dynamics without the
need for continuous incremental training. We further bring in a dynamic
evaluation setting for recommendation to mimic real-world dynamic scenarios and
bridge the offline-online gap to a better level. Our extensive experiments
including a large-scale industrial deployment showcases the lightweight plug-in
scalability of our GraphPL when integrated with various state-of-the-art
recommenders, emphasizing the advantages of GraphPL in terms of effectiveness,
robustness and efficiency.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: ETDPC: A Multimodality Framework for Classifying Pages in Electronic Theses and Dissertations
Abstract: Electronic theses and dissertations (ETDs) have been proposed, advocated, and
generated for more than 25 years. Although ETDs are hosted by commercial or
institutional digital library repositories, they are still an understudied type
of scholarly big data, partially because they are usually longer than
conference proceedings and journals. Segmenting ETDs will allow researchers to
study sectional content. Readers can navigate to particular pages of interest,
discover, and explore the content buried in these long documents. Most existing
frameworks on document page classification are designed for classifying general
documents and perform poorly on ETDs. In this paper, we propose ETDPC. Its
backbone is a two-stream multimodal model with a cross-attention network to
classify ETD pages into 13 categories. To overcome the challenge of imbalanced
labeled samples, we augmented data for minority categories and employed a
hierarchical classifier. ETDPC outperforms the state-of-the-art models in all
categories, achieving an F1 of 0.84 -- 0.96 for 9 out of 13 categories. We also
demonstrated its data efficiency. The code and data can be found on GitHub
(https://github.com/lamps-lab/ETDMiner/tree/master/etd_segmentation).

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Addressing Sample Inefficiency in Multi-View Representation Learning
Abstract: Non-contrastive self-supervised learning (NC-SSL) methods like BarlowTwins
and VICReg have shown great promise for label-free representation learning in
computer vision. Despite the apparent simplicity of these techniques,
researchers must rely on several empirical heuristics to achieve competitive
performance, most notably using high-dimensional projector heads and two
augmentations of the same image. In this work, we provide theoretical insights
on the implicit bias of the BarlowTwins and VICReg loss that can explain these
heuristics and guide the development of more principled recommendations. Our
first insight is that the orthogonality of the features is more critical than
projector dimensionality for learning good representations. Based on this, we
empirically demonstrate that low-dimensional projector heads are sufficient
with appropriate regularization, contrary to the existing heuristic. Our second
theoretical insight suggests that using multiple data augmentations better
represents the desiderata of the SSL objective. Based on this, we demonstrate
that leveraging more augmentations per sample improves representation quality
and trainability. In particular, it improves optimization convergence, leading
to better features emerging earlier in the training. Remarkably, we demonstrate
that we can reduce the pretraining dataset size by up to 4x while maintaining
accuracy and improving convergence simply by using more data augmentations.
Combining these insights, we present practical pretraining recommendations that
improve wall-clock time by 2x and improve performance on CIFAR-10/STL-10
datasets using a ResNet-50 backbone. Thus, this work provides a theoretical
insight into NC-SSL and produces practical recommendations for enhancing its
sample and compute efficiency.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
Abstract: Large language models (LLMs) can potentially democratize access to medical
knowledge. While many efforts have been made to harness and improve LLMs'
medical knowledge and reasoning capacities, the resulting models are either
closed-source (e.g., PaLM, GPT-4) or limited in scale (<= 13B parameters),
which restricts their abilities. In this work, we improve access to large-scale
medical LLMs by releasing MEDITRON: a suite of open-source LLMs with 7B and 70B
parameters adapted to the medical domain. MEDITRON builds on Llama-2 (through
our adaptation of Nvidia's Megatron-LM distributed trainer), and extends
pretraining on a comprehensively curated medical corpus, including selected
PubMed articles, abstracts, and internationally-recognized medical guidelines.
Evaluations using four major medical benchmarks show significant performance
gains over several state-of-the-art baselines before and after task-specific
finetuning. Overall, MEDITRON achieves a 6% absolute performance gain over the
best public baseline in its parameter class and 3% over the strongest baseline
we finetuned from Llama-2. Compared to closed-source LLMs, MEDITRON-70B
outperforms GPT-3.5 and Med-PaLM and is within 5% of GPT-4 and 10% of
Med-PaLM-2. We release our code for curating the medical pretraining corpus and
the MEDITRON model weights to drive open-source development of more capable
medical LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Joint Learning of Local and Global Features for Aspect-based Sentiment Classification
Abstract: Aspect-based sentiment classification (ASC) aims to judge the sentiment
polarity conveyed by the given aspect term in a sentence. The sentiment
polarity is not only determined by the local context but also related to the
words far away from the given aspect term. Most recent efforts related to the
attention-based models can not sufficiently distinguish which words they should
pay more attention to in some cases. Meanwhile, graph-based models are coming
into ASC to encode syntactic dependency tree information. But these models do
not fully leverage syntactic dependency trees as they neglect to incorporate
dependency relation tag information into representation learning effectively.
In this paper, we address these problems by effectively modeling the local and
global features. Firstly, we design a local encoder containing: a Gaussian mask
layer and a covariance self-attention layer. The Gaussian mask layer tends to
adjust the receptive field around aspect terms adaptively to deemphasize the
effects of unrelated words and pay more attention to local information. The
covariance self-attention layer can distinguish the attention weights of
different words more obviously. Furthermore, we propose a dual-level graph
attention network as a global encoder by fully employing dependency tag
information to capture long-distance information effectively. Our model
achieves state-of-the-art performance on both SemEval 2014 and Twitter
datasets.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: MixTEA: Semi-supervised Entity Alignment with Mixture Teaching
Abstract: Semi-supervised entity alignment (EA) is a practical and challenging task
because of the lack of adequate labeled mappings as training data. Most works
address this problem by generating pseudo mappings for unlabeled entities.
However, they either suffer from the erroneous (noisy) pseudo mappings or
largely ignore the uncertainty of pseudo mappings. In this paper, we propose a
novel semi-supervised EA method, termed as MixTEA, which guides the model
learning with an end-to-end mixture teaching of manually labeled mappings and
probabilistic pseudo mappings. We firstly train a student model using few
labeled mappings as standard. More importantly, in pseudo mapping learning, we
propose a bi-directional voting (BDV) strategy that fuses the alignment
decisions in different directions to estimate the uncertainty via the joint
matching confidence score. Meanwhile, we also design a matching diversity-based
rectification (MDR) module to adjust the pseudo mapping learning, thus reducing
the negative influence of noisy mappings. Extensive results on benchmark
datasets as well as further analyses demonstrate the superiority and the
effectiveness of our proposed method.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Enhancing Actuarial Non-Life Pricing Models via Transformers
Abstract: Currently, there is a lot of research in the field of neural networks for
non-life insurance pricing. The usual goal is to improve the predictive power
via neural networks while building upon the generalized linear model, which is
the current industry standard. Our paper contributes to this current journey
via novel methods to enhance actuarial non-life models with transformer models
for tabular data. We build here upon the foundation laid out by the combined
actuarial neural network as well as the localGLMnet and enhance those models
via the feature tokenizer transformer. The manuscript demonstrates the
performance of the proposed methods on a real-world claim frequency dataset and
compares them with several benchmark models such as generalized linear models,
feed-forward neural networks, combined actuarial neural networks, LocalGLMnet,
and pure feature tokenizer transformer. The paper shows that the new methods
can achieve better results than the benchmark models while preserving certain
generalized linear model advantages. The paper also discusses the practical
implications and challenges of applying transformer models in actuarial
settings.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Resource Constrained Semantic Segmentation for Waste Sorting
Abstract: This work addresses the need for efficient waste sorting strategies in
Materials Recovery Facilities to minimize the environmental impact of rising
waste. We propose resource-constrained semantic segmentation models for
segmenting recyclable waste in industrial settings. Our goal is to develop
models that fit within a 10MB memory constraint, suitable for edge applications
with limited processing capacity. We perform the experiments on three networks:
ICNet, BiSeNet (Xception39 backbone), and ENet. Given the aforementioned
limitation, we implement quantization and pruning techniques on the broader
nets, achieving positive results while marginally impacting the Mean IoU
metric. Furthermore, we propose a combination of Focal and Lov\'asz loss that
addresses the implicit class imbalance resulting in better performance compared
with the Cross-entropy loss function.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations
Abstract: There is an increasing consensus about the effectiveness of user-centred
approaches in the explainable artificial intelligence (XAI) field. Indeed, the
number and complexity of personalised and user-centred approaches to XAI have
rapidly grown in recent years. Often, these works have a two-fold objective:
(1) proposing novel XAI techniques able to consider the users and (2) assessing
the \textit{goodness} of such techniques with respect to others. From these new
works, it emerged that user-centred approaches to XAI positively affect the
interaction between users and systems. However, so far, the goodness of XAI
systems has been measured through indirect measures, such as performance. In
this paper, we propose an assessment task to objectively and quantitatively
measure the goodness of XAI systems in terms of their \textit{information
power}, which we intended as the amount of information the system provides to
the users during the interaction. Moreover, we plan to use our task to
objectively compare two XAI techniques in a human-robot decision-making task to
understand deeper whether user-centred approaches are more informative than
classical ones.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Isometric Motion Manifold Primitives
Abstract: The Motion Manifold Primitive (MMP) produces, for a given task, a continuous
manifold of trajectories each of which can successfully complete the task. It
consists of the decoder function that parametrizes the manifold and the
probability density in the latent coordinate space. In this paper, we first
show that the MMP performance can significantly degrade due to the geometric
distortion in the latent space -- by distortion, we mean that similar motions
are not located nearby in the latent space. We then propose {\it Isometric
Motion Manifold Primitives (IMMP)} whose latent coordinate space preserves the
geometry of the manifold. For this purpose, we formulate and use a Riemannian
metric for the motion space (i.e., parametric curve space), which we call a
{\it CurveGeom Riemannian metric}. Experiments with planar obstacle-avoiding
motions and pushing manipulation tasks show that IMMP significantly outperforms
existing MMP methods. Code is available at
https://github.com/Gabe-YHLee/IMMP-public.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: CRISPR: Eliminating Bias Neurons from an Instruction-following Language Model
Abstract: Large language models (LLMs) executing tasks through instruction-based
prompts often face challenges stemming from distribution differences between
user instructions and training instructions. This leads to distractions and
biases, especially when dealing with inconsistent dynamic labels. In this
paper, we introduces a novel bias mitigation method, CRISPR, designed to
alleviate instruction-label biases in LLMs. CRISPR utilizes attribution methods
to identify bias neurons influencing biased outputs and employs pruning to
eliminate the bias neurons. Experimental results demonstrate the method's
effectiveness in mitigating biases in instruction-based prompting, enhancing
language model performance on social bias benchmarks without compromising
pre-existing knowledge. CRISPR proves highly practical, model-agnostic,
offering flexibility in adapting to evolving social biases.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Visually Grounded Language Learning: a review of language games, datasets, tasks, and models
Abstract: In recent years, several machine learning models have been proposed. They are
trained with a language modelling objective on large-scale text-only data. With
such pretraining, they can achieve impressive results on many Natural Language
Understanding and Generation tasks. However, many facets of meaning cannot be
learned by ``listening to the radio" only. In the literature, many
Vision+Language (V+L) tasks have been defined with the aim of creating models
that can ground symbols in the visual modality. In this work, we provide a
systematic literature review of several tasks and models proposed in the V+L
field. We rely on Wittgenstein's idea of `language games' to categorise such
tasks into 3 different families: 1) discriminative games, 2) generative games,
and 3) interactive games. Our analysis of the literature provides evidence that
future work should be focusing on interactive games where communication in
Natural Language is important to resolve ambiguities about object referents and
action plans and that physical embodiment is essential to understand the
semantics of situations and events. Overall, these represent key requirements
for developing grounded meanings in neural models.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: TimeDRL: Disentangled Representation Learning for Multivariate Time-Series
Abstract: Multivariate time-series data in numerous real-world applications (e.g.,
healthcare and industry) are informative but challenging due to the lack of
labels and high dimensionality. Recent studies in self-supervised learning have
shown their potential in learning rich representations without relying on
labels, yet they fall short in learning disentangled embeddings and addressing
issues of inductive bias (e.g., transformation-invariance). To tackle these
challenges, we propose TimeDRL, a generic multivariate time-series
representation learning framework with disentangled dual-level embeddings.
TimeDRL is characterized by three novel features: (i) disentangled derivation
of timestamp-level and instance-level embeddings from patched time-series data
using a [CLS] token strategy; (ii) utilization of timestamp-predictive and
instance-contrastive tasks for disentangled representation learning, with the
former optimizing timestamp-level embeddings with predictive loss, and the
latter optimizing instance-level embeddings with contrastive loss; and (iii)
avoidance of augmentation methods to eliminate inductive biases, such as
transformation-invariance from cropping and masking. Comprehensive experiments
on 6 time-series forecasting datasets and 5 time-series classification datasets
have shown that TimeDRL consistently surpasses existing representation learning
approaches, achieving an average improvement of forecasting by 57.98% in MSE
and classification by 1.25% in accuracy. Furthermore, extensive ablation
studies confirmed the relative contribution of each component in TimeDRL's
architecture, and semi-supervised learning evaluations demonstrated its
effectiveness in real-world scenarios, even with limited labeled data.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Grounding Visual Illusions in Language: Do Vision-Language Models Perceive Illusions Like Humans?
Abstract: Vision-Language Models (VLMs) are trained on vast amounts of data captured by
humans emulating our understanding of the world. However, known as visual
illusions, human's perception of reality isn't always faithful to the physical
world. This raises a key question: do VLMs have the similar kind of illusions
as humans do, or do they faithfully learn to represent reality? To investigate
this question, we build a dataset containing five types of visual illusions and
formulate four tasks to examine visual illusions in state-of-the-art VLMs. Our
findings have shown that although the overall alignment is low, larger models
are closer to human perception and more susceptible to visual illusions. Our
dataset and initial findings will promote a better understanding of visual
illusions in humans and machines and provide a stepping stone for future
computational models that can better align humans and machines in perceiving
and communicating about the shared visual world. The code and data are
available at https://github.com/vl-illusion/dataset.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Injecting linguistic knowledge into BERT for Dialogue State Tracking
Abstract: Dialogue State Tracking (DST) models often employ intricate neural network
architectures, necessitating substantial training data, and their inference
processes lack transparency. This paper proposes a method that extracts
linguistic knowledge via an unsupervised framework and subsequently utilizes
this knowledge to augment BERT's performance and interpretability in DST tasks.
The knowledge extraction procedure is computationally economical and does not
necessitate annotations or additional training data. The injection of the
extracted knowledge necessitates the addition of only simple neural modules. We
employ the Convex Polytopic Model (CPM) as a feature extraction tool for DST
tasks and illustrate that the acquired features correlate with the syntactic
and semantic patterns in the dialogues. This correlation facilitates a
comprehensive understanding of the linguistic features influencing the DST
model's decision-making process. We benchmark this framework on various DST
tasks and observe a notable improvement in accuracy.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation
Abstract: Off-Policy Evaluation (OPE) aims to assess the effectiveness of
counterfactual policies using only offline logged data and is often used to
identify the top-k promising policies for deployment in online A/B tests.
Existing evaluation metrics for OPE estimators primarily focus on the
"accuracy" of OPE or that of downstream policy selection, neglecting
risk-return tradeoff in the subsequent online policy deployment. To address
this issue, we draw inspiration from portfolio evaluation in finance and
develop a new metric, called SharpeRatio@k, which measures the risk-return
tradeoff of policy portfolios formed by an OPE estimator under varying online
evaluation budgets (k). We validate our metric in two example scenarios,
demonstrating its ability to effectively distinguish between low-risk and
high-risk estimators and to accurately identify the most efficient estimator.
This efficient estimator is characterized by its capability to form the most
advantageous policy portfolios, maximizing returns while minimizing risks
during online deployment, a nuance that existing metrics typically overlook. To
facilitate a quick, accurate, and consistent evaluation of OPE via
SharpeRatio@k, we have also integrated this metric into an open-source
software, SCOPE-RL. Employing SharpeRatio@k and SCOPE-RL, we conduct
comprehensive benchmarking experiments on various estimators and RL tasks,
focusing on their risk-return tradeoff. These experiments offer several
interesting directions and suggestions for future OPE research.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: LLMs Accelerate Annotation for Medical Information Extraction
Abstract: The unstructured nature of clinical notes within electronic health records
often conceals vital patient-related information, making it challenging to
access or interpret. To uncover this hidden information, specialized Natural
Language Processing (NLP) models are required. However, training these models
necessitates large amounts of labeled data, a process that is both
time-consuming and costly when relying solely on human experts for annotation.
In this paper, we propose an approach that combines Large Language Models
(LLMs) with human expertise to create an efficient method for generating ground
truth labels for medical text annotation. By utilizing LLMs in conjunction with
human annotators, we significantly reduce the human annotation burden, enabling
the rapid creation of labeled datasets. We rigorously evaluate our method on a
medical information extraction task, demonstrating that our approach not only
substantially cuts down on human intervention but also maintains high accuracy.
The results highlight the potential of using LLMs to improve the utilization of
unstructured clinical data, allowing for the swift deployment of tailored NLP
solutions in healthcare.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning
Abstract: Assurance Cases (ACs) are an established approach in safety engineering to
argue quality claims in a structured way. In the context of quality assurance
for Machine Learning (ML)-based software components, ACs are also being
discussed and appear promising. Tools for operationalizing ACs do exist, yet
mainly focus on supporting safety engineers on the system level. However,
assuring the quality of an ML component within the system is commonly the
responsibility of data scientists, who are usually less familiar with these
tools. To address this gap, we propose a framework to support the
operationalization of ACs for ML components based on technologies that data
scientists use on a daily basis: Python and Jupyter Notebook. Our aim is to
make the process of creating ML-related evidence in ACs more effective. Results
from the application of the framework, documented through notebooks, can be
integrated into existing AC tools. We illustrate the application of the
framework on an example excerpt concerned with the quality of the test data.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Creative Agents: Empowering Agents with Imagination for Creative Tasks
Abstract: We study building embodied agents for open-ended creative tasks. While
existing methods build instruction-following agents that can perform diverse
open-ended tasks, none of them demonstrates creativity -- the ability to give
novel and diverse task solutions implicit in the language instructions. This
limitation comes from their inability to convert abstract language instructions
into concrete task goals in the environment and perform long-horizon planning
for such complicated goals. Given the observation that humans perform creative
tasks with the help of imagination, we propose a class of solutions for
creative agents, where the controller is enhanced with an imaginator that
generates detailed imaginations of task outcomes conditioned on language
instructions. We introduce several approaches to implementing the components of
creative agents. We implement the imaginator with either a large language model
for textual imagination or a diffusion model for visual imagination. The
controller can either be a behavior-cloning policy learned from data or a
pre-trained foundation model generating executable codes in the environment. We
benchmark creative tasks with the challenging open-world game Minecraft, where
the agents are asked to create diverse buildings given free-form language
instructions. In addition, we propose novel evaluation metrics for open-ended
creative tasks utilizing GPT-4V, which holds many advantages over existing
metrics. We perform a detailed experimental analysis of creative agents,
showing that creative agents are the first AI agents accomplishing diverse
building creation in the survival mode of Minecraft. Our benchmark and models
are open-source for future research on creative agents
(https://github.com/PKU-RL/Creative-Agents).

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: CHAIN: Exploring Global-Local Spatio-Temporal Information for Improved Self-Supervised Video Hashing
Abstract: Compressing videos into binary codes can improve retrieval speed and reduce
storage overhead. However, learning accurate hash codes for video retrieval can
be challenging due to high local redundancy and complex global dependencies
between video frames, especially in the absence of labels. Existing
self-supervised video hashing methods have been effective in designing
expressive temporal encoders, but have not fully utilized the temporal dynamics
and spatial appearance of videos due to less challenging and unreliable
learning tasks. To address these challenges, we begin by utilizing the
contrastive learning task to capture global spatio-temporal information of
videos for hashing. With the aid of our designed augmentation strategies, which
focus on spatial and temporal variations to create positive pairs, the learning
framework can generate hash codes that are invariant to motion, scale, and
viewpoint. Furthermore, we incorporate two collaborative learning tasks, i.e.,
frame order verification and scene change regularization, to capture local
spatio-temporal details within video frames, thereby enhancing the perception
of temporal structure and the modeling of spatio-temporal relationships. Our
proposed Contrastive Hashing with Global-Local Spatio-temporal Information
(CHAIN) outperforms state-of-the-art self-supervised video hashing methods on
four video benchmark datasets. Our codes will be released.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Remembering to Be Fair: On Non-Markovian Fairness in Sequential DecisionMaking (Preliminary Report)
Abstract: Fair decision making has largely been studied with respect to a single
decision. In this paper we investigate the notion of fairness in the context of
sequential decision making where multiple stakeholders can be affected by the
outcomes of decisions, and where decision making may be informed by additional
constraints and criteria beyond the requirement of fairness. In this setting,
we observe that fairness often depends on the history of the sequential
decision-making process and not just on the current state. To advance our
understanding of this class of fairness problems, we define the notion of
non-Markovian fairness in the context of sequential decision making. We
identify properties of non-Markovian fairness, including notions of long-term,
anytime, periodic, and bounded fairness. We further explore the interplay
between non-Markovian fairness and memory, and how this can support
construction of fair policies in sequential decision-making settings.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Correlation and Unintended Biases on Univariate and Multivariate Decision Trees
Abstract: Decision Trees are accessible, interpretable, and well-performing
classification models. A plethora of variants with increasing expressiveness
has been proposed in the last forty years. We contrast the two families of
univariate DTs, whose split functions partition data through axis-parallel
hyperplanes, and multivariate DTs, whose splits instead partition data through
oblique hyperplanes. The latter include the former, hence multivariate DTs are
in principle more powerful. Surprisingly enough, however, univariate DTs
consistently show comparable performances in the literature. We analyze the
reasons behind this, both with synthetic and real-world benchmark datasets. Our
research questions test whether the pre-processing phase of removing
correlation among features in datasets has an impact on the relative
performances of univariate vs multivariate DTs. We find that existing benchmark
datasets are likely biased towards favoring univariate DTs.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: LLM aided semi-supervision for Extractive Dialog Summarization
Abstract: Generating high-quality summaries for chat dialogs often requires large
labeled datasets. We propose a method to efficiently use unlabeled data for
extractive summarization of customer-agent dialogs. In our method, we frame
summarization as a question-answering problem and use state-of-the-art large
language models (LLMs) to generate pseudo-labels for a dialog. We then use
these pseudo-labels to fine-tune a chat summarization model, effectively
transferring knowledge from the large LLM into a smaller specialized model. We
demonstrate our method on the \tweetsumm dataset, and show that using 10% of
the original labelled data set we can achieve 65.9/57.0/61.0 ROUGE-1/-2/-L,
whereas the current state-of-the-art trained on the entire training data set
obtains 65.16/55.81/64.37 ROUGE-1/-2/-L. In other words, in the worst case
(i.e., ROUGE-L) we still effectively retain 94.7% of the performance while
using only 10% of the data.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Open-Set Object Recognition Using Mechanical Properties During Interaction
Abstract: while most of the tactile robots are operated in close-set conditions, it is
challenging for them to operate in open-set conditions where test objects are
beyond the robots' knowledge. We proposed an open-set recognition framework
using mechanical properties to recongise known objects and incrementally label
novel objects. The main contribution is a clustering algorithm that exploits
knowledge of known objects to estimate cluster centre and sizes, unlike a
typical algorithm that randomly selects them. The framework is validated with
the mechanical properties estimated from a real object during interaction. The
results show that the framework could recognise objects better than alternative
methods contributed by the novelty detector. Importantly, our clustering
algorithm yields better clustering performance than other methods. Furthermore,
the hyperparameters studies show that cluster size is important to clustering
results and needed to be tuned properly.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing
Abstract: GUI testing checks if a software system behaves as expected when users
interact with its graphical interface, e.g., testing specific functionality or
validating relevant use case scenarios. Currently, deciding what to test at
this high level is a manual task since automated GUI testing tools target lower
level adequacy metrics such as structural code coverage or activity coverage.
We propose DroidAgent, an autonomous GUI testing agent for Android, for
semantic, intent-driven automation of GUI testing. It is based on Large
Language Models and support mechanisms such as long- and short-term memory.
Given an Android app, DroidAgent sets relevant task goals and subsequently
tries to achieve them by interacting with the app. Our empirical evaluation of
DroidAgent using 15 apps from the Themis benchmark shows that it can set up and
perform realistic tasks, with a higher level of autonomy. For example, when
testing a messaging app, DroidAgent created a second account and added a first
account as a friend, testing a realistic use case, without human intervention.
On average, DroidAgent achieved 61% activity coverage, compared to 51% for
current state-of-the-art GUI testing techniques. Further, manual analysis shows
that 317 out of the 374 autonomously created tasks are realistic and relevant
to app functionalities, and also that DroidAgent interacts deeply with the apps
and covers more features.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Tailoring Mixup to Data using Kernel Warping functions
Abstract: Data augmentation is an essential building block for learning efficient deep
learning models. Among all augmentation techniques proposed so far, linear
interpolation of training data points, also called mixup, has found to be
effective for a large panel of applications. While the majority of works have
focused on selecting the right points to mix, or applying complex non-linear
interpolation, we are interested in mixing similar points more frequently and
strongly than less similar ones. To this end, we propose to dynamically change
the underlying distribution of interpolation coefficients through warping
functions, depending on the similarity between data points to combine. We
define an efficient and flexible framework to do so without losing in
diversity. We provide extensive experiments for classification and regression
tasks, showing that our proposed method improves both performance and
calibration of models. Code available in
https://github.com/ENSTA-U2IS/torch-uncertainty

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers
Abstract: Logical reasoning, i.e., deductively inferring the truth value of a
conclusion from a set of premises, is an important task for artificial
intelligence with wide potential impacts on science, mathematics, and society.
While many prompting-based strategies have been proposed to enable Large
Language Models (LLMs) to do such reasoning more effectively, they still appear
unsatisfactory, often failing in subtle and unpredictable ways. In this work,
we investigate the validity of instead reformulating such tasks as modular
neurosymbolic programming, which we call LINC: Logical Inference via
Neurosymbolic Computation. In LINC, the LLM acts as a semantic parser,
translating premises and conclusions from natural language to expressions in
first-order logic. These expressions are then offloaded to an external theorem
prover, which symbolically performs deductive inference. Leveraging this
approach, we observe significant performance gains on FOLIO and a balanced
subset of ProofWriter for three different models in nearly all experimental
conditions we evaluate. On ProofWriter, augmenting the comparatively small
open-source StarCoder+ (15.5B parameters) with LINC even outperforms GPT-3.5
and GPT-4 with Chain-of-Thought (CoT) prompting by an absolute 38% and 10%,
respectively. When used with GPT-4, LINC scores 26% higher than CoT on
ProofWriter while performing comparatively on FOLIO. Further analysis reveals
that although both methods on average succeed roughly equally often on this
dataset, they exhibit distinct and complementary failure modes. We thus provide
promising evidence for how logical reasoning over natural language can be
tackled through jointly leveraging LLMs alongside symbolic provers. All
corresponding code is publicly available at https://github.com/benlipkin/linc

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Unsupervised Temporal Action Localization via Self-paced Incremental Learning
Abstract: Recently, temporal action localization (TAL) has garnered significant
interest in information retrieval community. However, existing
supervised/weakly supervised methods are heavily dependent on extensive labeled
temporal boundaries and action categories, which is labor-intensive and
time-consuming. Although some unsupervised methods have utilized the
``iteratively clustering and localization'' paradigm for TAL, they still suffer
from two pivotal impediments: 1) unsatisfactory video clustering confidence,
and 2) unreliable video pseudolabels for model training. To address these
limitations, we present a novel self-paced incremental learning model to
enhance clustering and localization training simultaneously, thereby
facilitating more effective unsupervised TAL. Concretely, we improve the
clustering confidence through exploring the contextual feature-robust visual
information. Thereafter, we design two (constant- and variable- speed)
incremental instance learning strategies for easy-to-hard model training, thus
ensuring the reliability of these video pseudolabels and further improving
overall localization performance. Extensive experiments on two public datasets
have substantiated the superiority of our model over several state-of-the-art
competitors.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Caregiver Talk Shapes Toddler Vision: A Computational Study of Dyadic Play
Abstract: Infants' ability to recognize and categorize objects develops gradually. The
second year of life is marked by both the emergence of more semantic visual
representations and a better understanding of word meaning. This suggests that
language input may play an important role in shaping visual representations.
However, even in suitable contexts for word learning like dyadic play sessions,
caregivers utterances are sparse and ambiguous, often referring to objects that
are different from the one to which the child attends. Here, we systematically
investigate to what extent caregivers' utterances can nevertheless enhance
visual representations. For this we propose a computational model of visual
representation learning during dyadic play. We introduce a synthetic dataset of
ego-centric images perceived by a toddler-agent that moves and rotates toy
objects in different parts of its home environment while hearing caregivers'
utterances, modeled as captions. We propose to model toddlers' learning as
simultaneously aligning representations for 1) close-in-time images and 2)
co-occurring images and utterances. We show that utterances with statistics
matching those of real caregivers give rise to representations supporting
improved category recognition. Our analysis reveals that a small
decrease/increase in object-relevant naming frequencies can drastically impact
the learned representations. This affects the attention on object names within
an utterance, which is required for efficient visuo-linguistic alignment.
Overall, our results support the hypothesis that caregivers' naming utterances
can improve toddlers' visual representations.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Distributed AI in Zero-touch Provisioning for Edge Networks: Challenges and Research Directions
Abstract: Zero-touch network is anticipated to inaugurate the generation of intelligent
and highly flexible resource provisioning strategies where multiple service
providers collaboratively offer computation and storage resources. This
transformation presents substantial challenges to network administration and
service providers regarding sustainability and scalability. This article
combines Distributed Artificial Intelligence (DAI) with Zero-touch Provisioning
(ZTP) for edge networks. This combination helps to manage network devices
seamlessly and intelligently by minimizing human intervention. In addition,
several advantages are also highlighted that come with incorporating
Distributed AI into ZTP in the context of edge networks. Further, we draw
potential research directions to foster novel studies in this field and
overcome the current limitations.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: FacadeNet: Conditional Facade Synthesis via Selective Editing
Abstract: We introduce FacadeNet, a deep learning approach for synthesizing building
facade images from diverse viewpoints. Our method employs a conditional GAN,
taking a single view of a facade along with the desired viewpoint information
and generates an image of the facade from the distinct viewpoint. To precisely
modify view-dependent elements like windows and doors while preserving the
structure of view-independent components such as walls, we introduce a
selective editing module. This module leverages image embeddings extracted from
a pre-trained vision transformer. Our experiments demonstrated state-of-the-art
performance on building facade generation, surpassing alternative methods.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Data Scarcity in Recommendation Systems: A Survey
Abstract: The prevalence of online content has led to the widespread adoption of
recommendation systems (RSs), which serve diverse purposes such as news,
advertisements, and e-commerce recommendations. Despite their significance,
data scarcity issues have significantly impaired the effectiveness of existing
RS models and hindered their progress. To address this challenge, the concept
of knowledge transfer, particularly from external sources like pre-trained
language models, emerges as a potential solution to alleviate data scarcity and
enhance RS development. However, the practice of knowledge transfer in RSs is
intricate. Transferring knowledge between domains introduces data disparities,
and the application of knowledge transfer in complex RS scenarios can yield
negative consequences if not carefully designed. Therefore, this article
contributes to this discourse by addressing the implications of data scarcity
on RSs and introducing various strategies, such as data augmentation,
self-supervised learning, transfer learning, broad learning, and knowledge
graph utilization, to mitigate this challenge. Furthermore, it delves into the
challenges and future direction within the RS domain, offering insights that
are poised to facilitate the development and implementation of robust RSs,
particularly when confronted with data scarcity. We aim to provide valuable
guidance and inspiration for researchers and practitioners, ultimately driving
advancements in the field of RS.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation
Abstract: Despite remarkable advances that large language models have achieved in
chatbots, maintaining a non-toxic user-AI interactive environment has become
increasingly critical nowadays. However, previous efforts in toxicity detection
have been mostly based on benchmarks derived from social media content, leaving
the unique challenges inherent to real-world user-AI interactions
insufficiently explored. In this work, we introduce ToxicChat, a novel
benchmark based on real user queries from an open-source chatbot. This
benchmark contains the rich, nuanced phenomena that can be tricky for current
toxicity detection models to identify, revealing a significant domain
difference compared to social media content. Our systematic evaluation of
models trained on existing toxicity datasets has shown their shortcomings when
applied to this unique domain of ToxicChat. Our work illuminates the
potentially overlooked challenges of toxicity detection in real-world user-AI
conversations. In the future, ToxicChat can be a valuable resource to drive
further advancements toward building a safe and healthy environment for user-AI
interactions.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG
Abstract: Walking-assistive devices require adaptive control methods to ensure smooth
transitions between various modes of locomotion. For this purpose, detecting
human locomotion modes (e.g., level walking or stair ascent) in advance is
crucial for improving the intelligence and transparency of such robotic
systems. This study proposes Deep-STF, a unified end-to-end deep learning model
designed for integrated feature extraction in spatial, temporal, and frequency
dimensions from surface electromyography (sEMG) signals. Our model enables
accurate and robust continuous prediction of nine locomotion modes and 15
transitions at varying prediction time intervals, ranging from 100 to 500 ms.
In addition, we introduced the concept of 'stable prediction time' as a
distinct metric to quantify prediction efficiency. This term refers to the
duration during which consistent and accurate predictions of mode transitions
are made, measured from the time of the fifth correct prediction to the
occurrence of the critical event leading to the task transition. This
distinction between stable prediction time and prediction time is vital as it
underscores our focus on the precision and reliability of mode transition
predictions. Experimental results showcased Deep-STP's cutting-edge prediction
performance across diverse locomotion modes and transitions, relying solely on
sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other
machine learning techniques, achieving an outstanding average prediction
accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy
only marginally decreased to 93.00%. The averaged stable prediction times for
detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the
100-500 ms time advances.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Context Retrieval via Normalized Contextual Latent Interaction for Conversational Agent
Abstract: Conversational agents leveraging AI, particularly deep learning, are emerging
in both academic research and real-world applications. However, these
applications still face challenges, including disrespecting knowledge and
facts, not personalizing to user preferences, and enormous demand for
computational resources during training and inference. Recent research efforts
have been focused on addressing these challenges from various aspects,
including supplementing various types of auxiliary information to the
conversational agents. However, existing methods are still not able to
effectively and efficiently exploit relevant information from these auxiliary
supplements to further unleash the power of the conversational agents and the
language models they use. In this paper, we present a novel method, PK-NCLI,
that is able to accurately and efficiently identify relevant auxiliary
information to improve the quality of conversational responses by learning the
relevance among persona, chat history, and knowledge background through
low-level normalized contextual latent interaction. Our experimental results
indicate that PK-NCLI outperforms the state-of-the-art method, PK-FoCus, by
47.80%/30.61%/24.14% in terms of perplexity, knowledge grounding, and training
efficiency, respectively, and maintained the same level of persona grounding
performance. We also provide a detailed analysis of how different factors,
including language model choices and trade-offs on training weights, would
affect the performance of PK-NCLI.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences
Abstract: Distributed learning has emerged as a leading paradigm for training large
machine learning models. However, in real-world scenarios, participants may be
unreliable or malicious, posing a significant challenge to the integrity and
accuracy of the trained models. Byzantine fault tolerance mechanisms have been
proposed to address these issues, but they often assume full participation from
all clients, which is not always practical due to the unavailability of some
clients or communication constraints. In our work, we propose the first
distributed method with client sampling and provable tolerance to Byzantine
workers. The key idea behind the developed method is the use of gradient
clipping to control stochastic gradient differences in recursive variance
reduction. This allows us to bound the potential harm caused by Byzantine
workers, even during iterations when all sampled clients are Byzantine.
Furthermore, we incorporate communication compression into the method to
enhance communication efficiency. Under quite general assumptions, we prove
convergence rates for the proposed method that match the existing
state-of-the-art (SOTA) theoretical results.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: AlignBench: Benchmarking Chinese Alignment of Large Language Models
Abstract: Alignment has become a critical step for instruction-tuned Large Language
Models (LLMs) to become helpful assistants. However, effective evaluation of
alignment for emerging Chinese LLMs is still significantly lacking, calling for
real-scenario grounded, open-ended, challenging and automatic evaluations
tailored for alignment. To fill in this gap, we introduce AlignBench, a
comprehensive multi-dimensional benchmark for evaluating LLMs' alignment in
Chinese. Equipped with a human-in-the-loop data curation pipeline, our
benchmark employs a rule-calibrated multi-dimensional LLM-as-Judge with
Chain-of-Thought to generate explanations and final ratings as evaluations,
ensuring high reliability and interpretability. Furthermore, we report
AlignBench evaluated by CritiqueLLM, a dedicated Chinese evaluator LLM that
recovers 95% of GPT-4's evaluation ability. We will provide public APIs for
evaluating AlignBench with CritiqueLLM to facilitate the evaluation of LLMs'
Chinese alignment. All evaluation codes, data, and LLM generations are
available at \url{https://github.com/THUDM/AlignBench}.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor
Abstract: Federated learning encounters a critical challenge of data heterogeneity,
adversely affecting the performance and convergence of the federated model.
Various approaches have been proposed to address this issue, yet their
effectiveness is still limited. Recent studies have revealed that the federated
model suffers severe forgetting in local training, leading to global forgetting
and performance degradation. Although the analysis provides valuable insights,
a comprehensive understanding of the vulnerable classes and their impact
factors is yet to be established. In this paper, we aim to bridge this gap by
systematically analyzing the forgetting degree of each class during local
training across different communication rounds. Our observations are: (1) Both
missing and non-dominant classes suffer similar severe forgetting during local
training, while dominant classes show improvement in performance. (2) When
dynamically reducing the sample size of a dominant class, catastrophic
forgetting occurs abruptly when the proportion of its samples is below a
certain threshold, indicating that the local model struggles to leverage a few
samples of a specific class effectively to prevent forgetting. Motivated by
these findings, we propose a novel and straightforward algorithm called
Federated Knowledge Anchor (FedKA). Assuming that all clients have a single
shared sample for each class, the knowledge anchor is constructed before each
local training stage by extracting shared samples for missing classes and
randomly selecting one sample per class for non-dominant classes. The knowledge
anchor is then utilized to correct the gradient of each mini-batch towards the
direction of preserving the knowledge of the missing and non-dominant classes.
Extensive experimental results demonstrate that our proposed FedKA achieves
fast and stable convergence, significantly improving accuracy on popular
benchmarks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Beyond Average Return in Markov Decision Processes
Abstract: What are the functionals of the reward that can be computed and optimized
exactly in Markov Decision Processes? In the finite-horizon, undiscounted
setting, Dynamic Programming (DP) can only handle these operations efficiently
for certain classes of statistics. We summarize the characterization of these
classes for policy evaluation, and give a new answer for the planning problem.
Interestingly, we prove that only generalized means can be optimized exactly,
even in the more general framework of Distributional Reinforcement Learning
(DistRL).DistRL permits, however, to evaluate other functionals approximately.
We provide error bounds on the resulting estimators, and discuss the potential
of this approach as well as its limitations.These results contribute to
advancing the theory of Markov Decision Processes by examining overall
characteristics of the return, and particularly risk-conscious strategies.

Output: Artificial Intelligence