pwc-archive/papers-with-abstracts huggingface dataset:
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems
Authors: Jiechao Gao, Yuangang Li
Date: 2024-12-05 00:00:00
Tasks: Federated Learning, Meta-Learning, Transfer Learning

Personalized medication aims to tailor healthcare to individual patient characteristics. However, the heterogeneity of patient data across healthcare systems presents significant challenges to achieving accurate and effective personalized treatments. Ethical concerns further complicate the aggregation of large volumes of data from diverse institutions. Federated Learning (FL) offers a promising decentralized solution by enabling collaborative model training through the exchange of client models rather than raw data, thus preserving privacy. However, existing FL methods often suffer from retrogression during server aggregation, leading to a decline in model performance in real-world medical FL settings. To address data variability in distributed healthcare systems, we introduce Federated Meta-Learning for Personalized Medication (FedMetaMed), which combines federated learning and meta-learning to create models that adapt to diverse patient data across healthcare systems. The FedMetaMed framework aims to produce superior personalized models for individual clients by addressing these limitations. Specifically, we introduce Cumulative Fourier Aggregation (CFA) at the server to improve stability and effectiveness in global knowledge aggregation. CFA achieves this by gradually integrating client models from low to high frequencies. At the client level, we implement a Collaborative Transfer Optimization (CTO) strategy with a three-step process - Retrieve, Reciprocate, and Refine - to enhance the personalized local model through seamless global knowledge transfer. Experiments on real-world medical imaging datasets demonstrate that FedMetaMed outperforms state-of-the-art FL methods, showing superior generalization even on out-of-distribution cohorts.
How Good is ChatGPT in Giving Adaptive Guidance Using Knowledge Graphs in E-Learning Environments?
Authors: Patrick Ocheja, Brendan Flanagan, Yiling Dai, Hiroaki Ogata
Date: 2024-12-05 00:00:00
Tasks: Knowledge Graphs

E-learning environments are increasingly harnessing large language models (LLMs) like GPT-3.5 and GPT-4 for tailored educational support. This study introduces an approach that integrates dynamic knowledge graphs with LLMs to offer nuanced student assistance. By evaluating past and ongoing student interactions, the system identifies and appends the most salient learning context to prompts directed at the LLM. Central to this method is the knowledge graph's role in assessing a student's comprehension of topic prerequisites. Depending on the categorized understanding (good, average, or poor), the LLM adjusts its guidance, offering advanced assistance, foundational reviews, or in-depth prerequisite explanations, respectively. Preliminary findings suggest students could benefit from this tiered support, achieving enhanced comprehension and improved task outcomes. However, several issues related to potential errors arising from LLMs were identified, which can potentially mislead students. This highlights the need for human intervention to mitigate these risks. This research aims to advance AI-driven personalized learning while acknowledging the limitations and potential pitfalls, thus guiding future research in technology and data-driven education.
Fine-Grained Sentiment Analysis of Electric Vehicle User Reviews: A Bidirectional LSTM Approach to Capturing Emotional Intensity in Chinese Text
Authors: Shuhao Chen, Chengyi Tu
Date: 2024-12-05 00:00:00
Tasks: Sentiment Analysis

The rapid expansion of the electric vehicle (EV) industry has highlighted the importance of user feedback in improving product design and charging infrastructure. Traditional sentiment analysis methods often oversimplify the complexity of user emotions, limiting their effectiveness in capturing nuanced sentiments and emotional intensities. This study proposes a Bidirectional Long Short-Term Memory (Bi-LSTM) network-based sentiment scoring model to analyze user reviews of EV charging infrastructure. By assigning sentiment scores ranging from 0 to 5, the model provides a fine-grained understanding of emotional expression. Leveraging a dataset of 43,678 reviews from PC Auto, the study employs rigorous data cleaning and preprocessing, including tokenization and stop word removal, to optimize input for deep learning. The Bi-LSTM model demonstrates significant improvements over traditional approaches like SnowNLP across key evaluation metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Explained Variance Score (EVS). These results highlight the model's superior capability to capture nuanced sentiment dynamics, offering valuable insights for targeted product and service enhancements in the EV ecosystem.
A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications
Authors: Md. Ariful Islam, M. F. Mridha, Md Abrar Jahin, Nilanjan Dey
Date: 2024-12-05 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Fairness

The rapid advancement of deep learning has resulted in substantial advancements in AI-driven applications; however, the "black box" characteristic of these models frequently constrains their interpretability, transparency, and reliability. Explainable artificial intelligence (XAI) seeks to elucidate AI decision-making processes, guaranteeing that explanations faithfully represent the model's rationale and correspond with human comprehension. Despite comprehensive research in XAI, a significant gap persists in standardized procedures for assessing the efficacy and transparency of XAI techniques across many real-world applications. This study presents a unified XAI evaluation framework incorporating extensive quantitative and qualitative criteria to systematically evaluate the correctness, interpretability, robustness, fairness, and completeness of explanations generated by AI models. The framework prioritizes user-centric and domain-specific adaptations, hence improving the usability and reliability of AI models in essential domains. To address deficiencies in existing evaluation processes, we suggest defined benchmarks and a systematic evaluation pipeline that includes data loading, explanation development, and thorough method assessment. The suggested framework's relevance and variety are evidenced by case studies in healthcare, finance, agriculture, and autonomous systems. These provide a solid basis for the equitable and dependable assessment of XAI methodologies. This paradigm enhances XAI research by offering a systematic, flexible, and pragmatic method to guarantee transparency and accountability in AI systems across many real-world contexts.
Using SlowFast Networks for Near-Miss Incident Analysis in Dashcam Videos
Authors: Yucheng Zhang, Koichi Emura, Eiji Watanabe
Date: 2024-12-05 00:00:00

This paper classifies near-miss traffic videos using the SlowFast deep neural network that mimics the characteristics of the slow and fast visual information processed by two different streams from the M (Magnocellular) and P (Parvocellular) cells of the human brain. The approach significantly improves the accuracy of the traffic near-miss video analysis and presents insights into human visual perception in traffic scenarios. Moreover, it contributes to traffic safety enhancements and provides novel perspectives on the potential cognitive errors in traffic accidents.
Chain-of-Thought in Large Language Models: Decoding, Projection, and Activation
Authors: Hao Yang, Qianghua Zhao, Lei LI
Date: 2024-12-05 00:00:00
Tasks: Retrieval

Chain-of-Thought prompting has significantly enhanced the reasoning capabilities of large language models, with numerous studies exploring factors influencing its performance. However, the underlying mechanisms remain poorly understood. To further demystify the operational principles, this work examines three key aspects: decoding, projection, and activation, aiming to elucidate the changes that occur within models when employing Chainof-Thought. Our findings reveal that LLMs effectively imitate exemplar formats while integrating them with their understanding of the question, exhibiting fluctuations in token logits during generation but ultimately producing a more concentrated logits distribution, and activating a broader set of neurons in the final layers, indicating more extensive knowledge retrieval compared to standard prompts. Our code and data will be publicly avialable when the paper is accepted.
Considerations Influencing Offense-Defense Dynamics From Artificial Intelligence
Authors: Giulio Corsi, Kyle Kilian, Richard Mallah
Date: 2024-12-05 00:00:00
Tasks: Decision Making

The rapid advancement of artificial intelligence (AI) technologies presents profound challenges to societal safety. As AI systems become more capable, accessible, and integrated into critical services, the dual nature of their potential is increasingly clear. While AI can enhance defensive capabilities in areas like threat detection, risk assessment, and automated security operations, it also presents avenues for malicious exploitation and large-scale societal harm, for example through automated influence operations and cyber attacks. Understanding the dynamics that shape AI's capacity to both cause harm and enhance protective measures is essential for informed decision-making regarding the deployment, use, and integration of advanced AI systems. This paper builds on recent work on offense-defense dynamics within the realm of AI, proposing a taxonomy to map and examine the key factors that influence whether AI systems predominantly pose threats or offer protective benefits to society. By establishing a shared terminology and conceptual foundation for analyzing these interactions, this work seeks to facilitate further research and discourse in this critical area.
SocialMind: LLM-based Proactive AR Social Assistive System with Human-like Perception for In-situ Live Interactions
Authors: Bufang Yang, Yunqi Guo, Lilin Xu, Zhenyu Yan, Hongkai Chen, Guoliang Xing, Xiaofan Jiang
Date: 2024-12-05 00:00:00

Social interactions are fundamental to human life. The recent emergence of large language models (LLMs)-based virtual assistants has demonstrated their potential to revolutionize human interactions and lifestyles. However, existing assistive systems mainly provide reactive services to individual users, rather than offering in-situ assistance during live social interactions with conversational partners. In this study, we introduce SocialMind, the first LLM-based proactive AR social assistive system that provides users with in-situ social assistance. SocialMind employs human-like perception leveraging multi-modal sensors to extract both verbal and nonverbal cues, social factors, and implicit personas, incorporating these social cues into LLM reasoning for social suggestion generation. Additionally, SocialMind employs a multi-tier collaborative generation strategy and proactive update mechanism to display social suggestions on Augmented Reality (AR) glasses, ensuring that suggestions are timely provided to users without disrupting the natural flow of conversation. Evaluations on three public datasets and a user study with 20 participants show that SocialMind achieves 38.3% higher engagement compared to baselines, and 95% of participants are willing to use SocialMind in their live social interactions.
From Code to Play: Benchmarking Program Search for Games Using Large Language Models
Authors: Manuel Eberhardinger, James Goodman, Alexander Dockhorn, Diego Perez-Liebana, Raluca D. Gaina, Duygu Çakmak, Setareh Maghsudi, Simon Lucas
Date: 2024-12-05 00:00:00
Tasks: Atari Games, Benchmarking, Program Synthesis, TAG

Large language models (LLMs) have shown impressive capabilities in generating program code, opening exciting opportunities for applying program synthesis to games. In this work, we explore the potential of LLMs to directly synthesize usable code for a wide range of gaming applications, focusing on two programming languages, Python and Java. We use an evolutionary hill-climbing algorithm, where the mutations and seeds of the initial programs are controlled by LLMs. For Python, the framework covers various game-related tasks, including five miniature versions of Atari games, ten levels of Baba is You, an environment inspired by Asteroids, and a maze generation task. For Java, the framework contains 12 games from the TAG tabletop games framework. Across 29 tasks, we evaluated 12 language models for Python and 8 for Java. Our findings suggest that the performance of LLMs depends more on the task than on model size. While larger models generate more executable programs, these do not always result in higher-quality solutions but are much more expensive. No model has a clear advantage, although on any specific task, one model may be better. Trying many models on a problem and using the best results across them is more reliable than using just one.
Expanding Deep Learning-based Sensing Systems with Multi-Source Knowledge Transfer
Authors: Gaole Dai, Huatao Xu, Rui Tan, Mo Li
Date: 2024-12-05 00:00:00
Tasks: Deep Learning, Knowledge Distillation, Transfer Learning

Expanding the existing sensing systems to provide high-quality deep learning models for more domains, such as new users or environments, is challenged by the limited labeled data and the data and device heterogeneities. While knowledge distillation methods could overcome label scarcity and device heterogeneity, they assume the teachers are fully reliable and overlook the data heterogeneity, which prevents the direct adoption of existing models. To address this problem, this paper proposes an efficient knowledge transfer framework, HaKT, to expand sensing systems. It first selects multiple high-quality models from the system at a low cost and then fuses their knowledge by assigning sample-wise weights to their predictions. Later, the fused knowledge is selectively injected into the customized models for new domains based on the knowledge quality. Extensive experiments on different tasks, modalities, and settings show that HaKT outperforms stat-of-the-art baselines by at most 16.5% accuracy and saves up to 39% communication traffic.
ProtDAT: A Unified Framework for Protein Sequence Design from Any Protein Text Description
Authors: Xiao-Yu Guo, Yi-Fan Li, YuAn Liu, Xiaoyong Pan, Hong-Bin Shen
Date: 2024-12-05 00:00:00
Tasks: Descriptive, Protein Design

Protein design has become a critical method in advancing significant potential for various applications such as drug development and enzyme engineering. However, protein design methods utilizing large language models with solely pretraining and fine-tuning struggle to capture relationships in multi-modal protein data. To address this, we propose ProtDAT, a de novo fine-grained framework capable of designing proteins from any descriptive protein text input. ProtDAT builds upon the inherent characteristics of protein data to unify sequences and text as a cohesive whole rather than separate entities. It leverages an innovative multi-modal cross-attention, integrating protein sequences and textual information for a foundational level and seamless integration. Experimental results demonstrate that ProtDAT achieves the state-of-the-art performance in protein sequence generation, excelling in rationality, functionality, structural similarity, and validity. On 20,000 text-sequence pairs from Swiss-Prot, it improves pLDDT by 6%, TM-score by 0.26, and reduces RMSD by 1.2 {\AA}, highlighting its potential to advance protein design.
Does your model understand genes? A benchmark of gene properties for biological and text models
Authors: Yoav Kan-Tor, Michael Morris Danziger, Eden Zohar, Matan Ninio, Yishai Shimoni
Date: 2024-12-05 00:00:00
Tasks: Benchmarking, Multi-class Classification

The application of deep learning methods, particularly foundation models, in biological research has surged in recent years. These models can be text-based or trained on underlying biological data, especially omics data of various types. However, comparing the performance of these models consistently has proven to be a challenge due to differences in training data and downstream tasks. To tackle this problem, we developed an architecture-agnostic benchmarking approach that, instead of evaluating the models directly, leverages entity representation vectors from each model and trains simple predictive models for each benchmarking task. This ensures that all types of models are evaluated using the same input and output types. Here we focus on gene properties collected from professionally curated bioinformatics databases. These gene properties are categorized into five major groups: genomic properties, regulatory functions, localization, biological processes, and protein properties. Overall, we define hundreds of tasks based on these databases, which include binary, multi-label, and multi-class classification tasks. We apply these benchmark tasks to evaluate expression-based models, large language models, protein language models, DNA-based models, and traditional baselines. Our findings suggest that text-based models and protein language models generally outperform expression-based models in genomic properties and regulatory functions tasks, whereas expression-based models demonstrate superior performance in localization tasks. These results should aid in the development of more informed artificial intelligence strategies for biological understanding and therapeutic discovery. To ensure the reproducibility and transparency of our findings, we have made the source code and benchmark data publicly accessible for further investigation and expansion at github.com/BiomedSciAI/gene-benchmark.
Practical Considerations for Agentic LLM Systems
Authors: Chris Sypherd, Vaishak Belle
Date: 2024-12-05 00:00:00

As the strength of Large Language Models (LLMs) has grown over recent years, so too has interest in their use as the underlying models for autonomous agents. Although LLMs demonstrate emergent abilities and broad expertise across natural language domains, their inherent unpredictability makes the implementation of LLM agents challenging, resulting in a gap between related research and the real-world implementation of such systems. To bridge this gap, this paper frames actionable insights and considerations from the research community in the context of established application paradigms to enable the construction and facilitate the informed deployment of robust LLM agents. Namely, we position relevant research findings into four broad categories--Planning, Memory, Tools, and Control Flow--based on common practices in application-focused literature and highlight practical considerations to make when designing agentic LLMs for real-world applications, such as handling stochasticity and managing resources efficiently. While we do not conduct empirical evaluations, we do provide the necessary background for discussing critical aspects of agentic LLM designs, both in academia and industry.
Enhancing Mathematical Reasoning in LLMs with Background Operators
Authors: Jiajun Chen, Yik-Cheung Tam
Date: 2024-12-05 00:00:00
Tasks: Data Augmentation, Math, Mathematical Reasoning

We propose utilizing background operators for mathematical reasoning in large language models (LLMs). To achieve this, we define a set of fundamental mathematical predicates as the basic building blocks. For each mathematical problem, we develop a Prolog solution that includes problem-specific predicates and intermediate predicates derived from these background operators, ensuring that each solution adheres to the defined operator set. We introduce the MATH-Prolog corpus, which is derived from the counting and probability categories of the MATH corpus. For efficient data augmentation, we apply K-fold cross-validated self-training. This method incrementally generates new Prolog solutions for each fold, incorporating those verified as correct into the training set throughout the model training process. Our experimental results demonstrate that 5-fold crossvalidated self-training effectively identifies new, accurate Prolog solutions, achieving an accuracy of 84.6% on the cross-validated set, and 84.8% on the test set during fine-tuning the Meta-Llama-3.1-8B-Instruct model. This approach successfully uncovers new solutions with fully computable inference steps for previously unseen problems. Additionally, incorporating the background mathematical predicates into the prompt enhances solution coverage.
Monet: Mixture of Monosemantic Experts for Transformers
Authors: Jungwoo Park, Young Jin Ahn, Kee-Eung Kim, Jaewoo Kang
Date: 2024-12-05 00:00:00
Tasks: Dictionary Learning, Mixture-of-Experts

Understanding the internal computations of large language models (LLMs) is crucial for aligning them with human values and preventing undesirable behaviors like toxic content generation. However, mechanistic interpretability is hindered by polysemanticity -- where individual neurons respond to multiple, unrelated concepts. While Sparse Autoencoders (SAEs) have attempted to disentangle these features through sparse dictionary learning, they have compromised LLM performance due to reliance on post-hoc reconstruction loss. To address this issue, we introduce Mixture of Monosemantic Experts for Transformers (Monet) architecture, which incorporates sparse dictionary learning directly into end-to-end Mixture-of-Experts pretraining. Our novel expert decomposition method enables scaling the expert count to 262,144 per layer while total parameters scale proportionally to the square root of the number of experts. Our analyses demonstrate mutual exclusivity of knowledge across experts and showcase the parametric knowledge encapsulated within individual experts. Moreover, Monet allows knowledge manipulation over domains, languages, and toxicity mitigation without degrading general performance. Our pursuit of transparent LLMs highlights the potential of scaling expert counts to enhance mechanistic interpretability and directly resect the internal knowledge to fundamentally adjust model behavior. The source code and pretrained checkpoints are available at https://github.com/dmis-lab/Monet.
Bench-CoE: a Framework for Collaboration of Experts from Benchmark
Authors: Yuanshuai Wang, Xingjian Zhang, Jinkun Zhao, Siwei Wen, Peilin Feng, Shuhao Liao, Lei Huang, Wenjun Wu
Date: 2024-12-05 00:00:00

Large Language Models (LLMs) are key technologies driving intelligent systems to handle multiple tasks. To meet the demands of various tasks, an increasing number of LLMs-driven experts with diverse capabilities have been developed, accompanied by corresponding benchmarks to evaluate their performance. This paper proposes the Bench-CoE framework, which enables Collaboration of Experts (CoE) by effectively leveraging benchmark evaluations to achieve optimal performance across various tasks. Bench-CoE includes a set of expert models, a router for assigning tasks to corresponding experts, and a benchmark dataset for training the router. Moreover, we formulate Query-Level and Subject-Level approaches based on our framework, and analyze the merits and drawbacks of these two approaches. Finally, we conduct a series of experiments with vary data distributions on both language and multimodal tasks to validate that our proposed Bench-CoE outperforms any single model in terms of overall performance. We hope this method serves as a baseline for further research in this area. The code is available at \url{https://github.com/ZhangXJ199/Bench-CoE}.