pwc-archive/papers-with-abstracts huggingface dataset:
Performance-Driven QUBO for Recommender Systems on Quantum Annealers
Authors: Jiayang Niu, Jie Li, Ke Deng, Mark Sanderson, Yongli Ren
Date: 2024-10-20 00:00:00
Tasks: counterfactual, feature selection, Recommendation Systems

We propose Counterfactual Analysis Quadratic Unconstrained Binary Optimization (CAQUBO) to solve QUBO problems for feature selection in recommender systems. CAQUBO leverages counterfactual analysis to measure the impact of individual features and feature combinations on model performance and employs the measurements to construct the coefficient matrix for a quantum annealer to select the optimal feature combinations for recommender systems, thereby improving their final recommendation performance. By establishing explicit connections between features and the recommendation performance, the proposed approach demonstrates superior performance compared to the state-of-the-art quantum annealing methods. Extensive experiments indicate that integrating quantum computing with counterfactual analysis holds great promise for addressing these challenges.
AI Can Enhance Creativity in Social Networks
Authors: Raiyan Abdul Baten, Ali Sarosh Bangash, Krish Veera, Gourab Ghoshal, Ehsan Hoque
Date: 2024-10-20 00:00:00

Can peer recommendation engines elevate people's creative performances in self-organizing social networks? Answering this question requires resolving challenges in data collection (e.g., tracing inspiration links and psycho-social attributes of nodes) and intervention design (e.g., balancing idea stimulation and redundancy in evolving information environments). We trained a model that predicts people's ideation performances using semantic and network-structural features in an online platform. Using this model, we built SocialMuse, which maximizes people's predicted performances to generate peer recommendations for them. We found treatment networks leveraging SocialMuse outperforming AI-agnostic control networks in several creativity measures. The treatment networks were more decentralized than the control, as SocialMuse increasingly emphasized network-structural features at large network sizes. This decentralization spreads people's inspiration sources, helping inspired ideas stand out better. Our study provides actionable insights into building intelligent systems for elevating creativity.
HyQE: Ranking Contexts with Hypothetical Query Embeddings
Authors: Weichao Zhou, Jiaxin Zhang, Hilaf Hasson, Anu Singh, Wenchao Li
Date: 2024-10-20 00:00:00
Tasks: Retrieval

In retrieval-augmented systems, context ranking techniques are commonly employed to reorder the retrieved contexts based on their relevance to a user query. A standard approach is to measure this relevance through the similarity between contexts and queries in the embedding space. However, such similarity often fails to capture the relevance. Alternatively, large language models (LLMs) have been used for ranking contexts. However, they can encounter scalability issues when the number of candidate contexts grows and the context window sizes of the LLMs remain constrained. Additionally, these approaches require fine-tuning LLMs with domain-specific data. In this work, we introduce a scalable ranking framework that combines embedding similarity and LLM capabilities without requiring LLM fine-tuning. Our framework uses a pre-trained LLM to hypothesize the user query based on the retrieved contexts and ranks the context based on the similarity between the hypothesized queries and the user query. Our framework is efficient at inference time and is compatible with many other retrieval and ranking techniques. Experimental results show that our method improves the ranking performance across multiple benchmarks. The complete code and data are available at https://github.com/zwc662/hyqe
Lossless KV Cache Compression to 2%
Authors: Zhen Yang, J. N. Han, Kan Wu, Ruobing Xie, An Wang, Xingwu Sun, Zhanhui Kang
Date: 2024-10-20 00:00:00
Tasks: Dimensionality Reduction, Quantization

Large language models have revolutionized data processing in numerous domains, with their ability to handle extended context reasoning receiving notable recognition. To speed up inference, maintaining a key-value (KV) cache memory is essential. Nonetheless, the growing demands for KV cache memory create significant hurdles for efficient implementation. This work introduces a novel architecture, Cross-Layer Latent Attention (CLLA), aimed at compressing the KV cache to less than 2% of its original size while maintaining comparable performance levels. CLLA integrates multiple aspects of KV cache compression, including attention head/dimension reduction, layer sharing, and quantization techniques, into a cohesive framework. Our extensive experiments demonstrate that CLLA achieves lossless performance on most tasks while utilizing minimal KV cache, marking a significant advancement in practical KV cache compression.
Tensor-Fused Multi-View Graph Contrastive Learning
Authors: Yujia Wu, Junyi Mo, Elynn Chen, Yuzhou Chen
Date: 2024-10-20 00:00:00
Tasks: Contrastive Learning, Graph Classification

Graph contrastive learning (GCL) has emerged as a promising approach to enhance graph neural networks' (GNNs) ability to learn rich representations from unlabeled graph-structured data. However, current GCL models face challenges with computational demands and limited feature utilization, often relying only on basic graph properties like node degrees and edge attributes. This constrains their capacity to fully capture the complex topological characteristics of real-world phenomena represented by graphs. To address these limitations, we propose Tensor-Fused Multi-View Graph Contrastive Learning (TensorMV-GCL), a novel framework that integrates extended persistent homology (EPH) with GCL representations and facilitates multi-scale feature extraction. Our approach uniquely employs tensor aggregation and compression to fuse information from graph and topological features obtained from multiple augmented views of the same graph. By incorporating tensor concatenation and contraction modules, we reduce computational overhead by separating feature tensor aggregation and transformation. Furthermore, we enhance the quality of learned topological features and model robustness through noise-injected EPH. Experiments on molecular, bioinformatic, and social network datasets demonstrate TensorMV-GCL's superiority, outperforming 15 state-of-the-art methods in graph classification tasks across 9 out of 11 benchmarks while achieving comparable results on the remaining two. The code for this paper is publicly available at https://github.com/CS-SAIL/Tensor-MV-GCL.git.
Economic Anthropology in the Era of Generative Artificial Intelligence
Authors: Zachary Sheldon, Peeyush Kumar
Date: 2024-10-20 00:00:00
Tasks: Decision Making

This paper explores the intersection of economic anthropology and generative artificial intelligence (GenAI). It examines how large language models (LLMs) can simulate human decision-making and the inductive biases present in AI research. The study introduces two AI models: C.A.L.L.O.N. (Conventionally Average Late Liberal ONtology) and M.A.U.S.S. (More Accurate Understanding of Society and its Symbols). The former is trained on standard data, while the latter is adapted with anthropological knowledge. The research highlights how anthropological training can enhance LLMs' ability to recognize diverse economic systems and concepts. The findings suggest that integrating economic anthropology with AI can provide a more pluralistic understanding of economics and improve the sustainability of non-market economic systems.
Jailbreaking and Mitigation of Vulnerabilities in Large Language Models
Authors: Benji Peng, Keyu Chen, Qian Niu, Ziqian Bi, Ming Liu, Pohsun Feng, Tianyang Wang, Lawrence K. Q. Yan, Yizhu Wen, Yichao Zhang, Caitlyn Heqi Yin
Date: 2024-10-20 00:00:00
Tasks: Natural Language Understanding

Large Language Models (LLMs) have transformed artificial intelligence by advancing natural language understanding and generation, enabling applications across fields beyond healthcare, software engineering, and conversational systems. Despite these advancements in the past few years, LLMs have shown considerable vulnerabilities, particularly to prompt injection and jailbreaking attacks. This review analyzes the state of research on these vulnerabilities and presents available defense strategies. We roughly categorize attack approaches into prompt-based, model-based, multimodal, and multilingual, covering techniques such as adversarial prompting, backdoor injections, and cross-modality exploits. We also review various defense mechanisms, including prompt filtering, transformation, alignment techniques, multi-agent defenses, and self-regulation, evaluating their strengths and shortcomings. We also discuss key metrics and benchmarks used to assess LLM safety and robustness, noting challenges like the quantification of attack success in interactive contexts and biases in existing datasets. Identifying current research gaps, we suggest future directions for resilient alignment strategies, advanced defenses against evolving attacks, automation of jailbreak detection, and consideration of ethical and societal impacts. This review emphasizes the need for continued research and cooperation within the AI community to enhance LLM security and ensure their safe deployment.
Bias Amplification: Language Models as Increasingly Biased Media
Authors: Ze Wang, Zekun Wu, Jeremy Zhang, Navya Jain, Xin Guan, Adriano Koshiyama
Date: 2024-10-19 00:00:00

As Large Language Models (LLMs) become increasingly integrated into various facets of society, a significant portion of online text consequently become synthetic. This raises concerns about bias amplification, a phenomenon where models trained on synthetic data amplify the pre-existing biases over successive training iterations. Previous literature seldom discusses bias amplification as an independent issue from model collapse. In this work, we address the gap in understanding the bias amplification of LLMs with four main contributions. Firstly, we propose a theoretical framework, defining the necessary and sufficient conditions for its occurrence, and emphasizing that it occurs independently of model collapse. Using statistical simulations with weighted maximum likelihood estimation, we demonstrate the framework and show how bias amplification arises without the sampling and functional form issues that typically drive model collapse. Secondly, we conduct experiments with GPT-2 to empirically demonstrate bias amplification, specifically examining open-ended generational political bias with a benchmark we developed. We observe that GPT-2 exhibits a right-leaning bias in sentence continuation tasks and that the bias progressively increases with iterative fine-tuning on synthetic data generated by previous iterations. Thirdly, we explore three potential mitigation strategies: Overfitting, Preservation, and Accumulation. We find that both Preservation and Accumulation effectively mitigate bias amplification and model collapse. Finally, using novel mechanistic interpretation techniques, we demonstrate that in the GPT-2 experiments, bias amplification and model collapse are driven by distinct sets of neurons, which aligns with our theoretical framework.
Chasing Random: Instruction Selection Strategies Fail to Generalize
Authors: Harshita Diddee, Daphne Ippolito
Date: 2024-10-19 00:00:00

Prior work has shown that language models can be tuned to follow user instructions using only a small set of high-quality instructions. This has accelerated the development of methods that filter a large, noisy instruction-tuning datasets down to high-quality subset which works just as well. However, typically, the performance of these methods is not demonstrated across a uniform experimental setup and thus their generalization capabilities are not well established. In this work, we analyze popular selection strategies across different source datasets, selection budgets and evaluation benchmarks: Our results indicate that selection strategies generalize poorly, often failing to consistently outperform even random baselines. We also analyze the cost-performance trade-offs of using data selection. Our findings reveal that data selection can often exceed the cost of fine-tuning on the full dataset, yielding only marginal and sometimes no gains compared to tuning on the full dataset or a random subset.
AutoFLUKA: A Large Language Model Based Framework for Automating Monte Carlo Simulations in FLUKA
Authors: Zavier Ndum Ndum, Jian Tao, John Ford, Yang Liu
Date: 2024-10-19 00:00:00
Tasks: AI Agent, Language Modeling, Language Modelling, Large Language Model, RAG

Monte Carlo (MC) simulations, particularly using FLUKA, are essential for replicating real-world scenarios across scientific and engineering fields. Despite the robustness and versatility, FLUKA faces significant limitations in automation and integration with external post-processing tools, leading to workflows with a steep learning curve, which are time-consuming and prone to human errors. Traditional methods involving the use of shell and Python scripts, MATLAB, and Microsoft Excel require extensive manual intervention and lack flexibility, adding complexity to evolving scenarios. This study explores the potential of Large Language Models (LLMs) and AI agents to address these limitations. AI agents, integrate natural language processing with autonomous reasoning for decision-making and adaptive planning, making them ideal for automation. We introduce AutoFLUKA, an AI agent application developed using the LangChain Python Framework to automate typical MC simulation workflows in FLUKA. AutoFLUKA can modify FLUKA input files, execute simulations, and efficiently process results for visualization, significantly reducing human labor and error. Our case studies demonstrate that AutoFLUKA can handle both generalized and domain-specific cases, such as Microdosimetry, with an streamlined automated workflow, showcasing its scalability and flexibility. The study also highlights the potential of Retrieval Augmentation Generation (RAG) tools to act as virtual assistants for FLUKA, further improving user experience, time and efficiency. In conclusion, AutoFLUKA represents a significant advancement in automating MC simulation workflows, offering a robust solution to the inherent limitations. This innovation not only saves time and resources but also opens new paradigms for research and development in high energy physics, medical physics, nuclear engineering space and environmental science.
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
Authors: Vindula Jayawardana, Baptiste Freydt, Ao Qu, Cameron Hickert, Zhongxia Yan, Cathy Wu
Date: 2024-10-19 00:00:00
Tasks: Benchmarking, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Despite the popularity of multi-agent reinforcement learning (RL) in simulated and two-player applications, its success in messy real-world applications has been limited. A key challenge lies in its generalizability across problem variations, a common necessity for many real-world problems. Contextual reinforcement learning (CRL) formalizes learning policies that generalize across problem variations. However, the lack of standardized benchmarks for multi-agent CRL has hindered progress in the field. Such benchmarks are desired to be based on real-world applications to naturally capture the many open challenges of real-world problems that affect generalization. To bridge this gap, we propose IntersectionZoo, a comprehensive benchmark suite for multi-agent CRL through the real-world application of cooperative eco-driving in urban road networks. The task of cooperative eco-driving is to control a fleet of vehicles to reduce fleet-level vehicular emissions. By grounding IntersectionZoo in a real-world application, we naturally capture real-world problem characteristics, such as partial observability and multiple competing objectives. IntersectionZoo is built on data-informed simulations of 16,334 signalized intersections derived from 10 major US cities, modeled in an open-source industry-grade microscopic traffic simulator. By modeling factors affecting vehicular exhaust emissions (e.g., temperature, road conditions, travel demand), IntersectionZoo provides one million data-driven traffic scenarios. Using these traffic scenarios, we benchmark popular multi-agent RL and human-like driving algorithms and demonstrate that the popular multi-agent RL algorithms struggle to generalize in CRL settings.
Low-cost Robust Night-time Aerial Material Segmentation through Hyperspectral Data and Sparse Spatio-Temporal Learning
Authors: Chandrajit Bajaj, Minh Nguyen, Shubham Bhardwaj
Date: 2024-10-19 00:00:00
Tasks: Material Segmentation, Segmentation, Time Series

Material segmentation is a complex task, particularly when dealing with aerial data in poor lighting and atmospheric conditions. To address this, hyperspectral data from specialized cameras can be very useful in addition to RGB images. However, due to hardware constraints, high spectral data often come with lower spatial resolution. Additionally, incorporating such data into a learning-based segmentation framework is challenging due to the numerous data channels involved. To overcome these difficulties, we propose an innovative Siamese framework that uses time series-based compression to effectively and scalably integrate the additional spectral data into the segmentation task. We demonstrate our model's effectiveness through competitive benchmarks on aerial datasets in various environmental conditions.
Medical-GAT: Cancer Document Classification Leveraging Graph-Based Residual Network for Scenarios with Limited Data
Authors: Elias Hossain, Tasfia Nuzhat, Shamsul Masum, Shahram Rahimi, Noorbakhsh Amiri Golilarz
Date: 2024-10-19 00:00:00
Tasks: Document Classification, Graph Attention

Accurate classification of cancer-related medical abstracts is crucial for healthcare management and research. However, obtaining large, labeled datasets in the medical domain is challenging due to privacy concerns and the complexity of clinical data. This scarcity of annotated data impedes the development of effective machine learning models for cancer document classification. To address this challenge, we present a curated dataset of 1,874 biomedical abstracts, categorized into thyroid cancer, colon cancer, lung cancer, and generic topics. Our research focuses on leveraging this dataset to improve classification performance, particularly in data-scarce scenarios. We introduce a Residual Graph Attention Network (R-GAT) with multiple graph attention layers that capture the semantic information and structural relationships within cancer-related documents. Our R-GAT model is compared with various techniques, including transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT), RoBERTa, and domain-specific models like BioBERT and Bio+ClinicalBERT. We also evaluated deep learning models (CNNs, LSTMs) and traditional machine learning models (Logistic Regression, SVM). Additionally, we explore ensemble approaches that combine deep learning models to enhance classification. Various feature extraction methods are assessed, including Term Frequency-Inverse Document Frequency (TF-IDF) with unigrams and bigrams, Word2Vec, and tokenizers from BERT and RoBERTa. The R-GAT model outperforms other techniques, achieving precision, recall, and F1 scores of 0.99, 0.97, and 0.98 for thyroid cancer; 0.96, 0.94, and 0.95 for colon cancer; 0.96, 0.99, and 0.97 for lung cancer; and 0.95, 0.96, and 0.95 for generic topics.
Augmented Lagrangian-Based Safe Reinforcement Learning Approach for Distribution System Volt/VAR Control
Authors: Guibin Chen
Date: 2024-10-19 00:00:00
Tasks: Reinforcement Learning (RL), Safe Reinforcement Learning

This paper proposes a data-driven solution for Volt-VAR control problem in active distribution system. As distribution system models are always inaccurate and incomplete, it is quite difficult to solve the problem. To handle with this dilemma, this paper formulates the Volt-VAR control problem as a constrained Markov decision process (CMDP). By synergistically combining the augmented Lagrangian method and soft actor critic algorithm, a novel safe off-policy reinforcement learning (RL) approach is proposed in this paper to solve the CMDP. The actor network is updated in a policy gradient manner with the Lagrangian value function. A double-critics network is adopted to synchronously estimate the action-value function to avoid overestimation bias. The proposed algorithm does not require strong convexity guarantee of examined problems and is sample efficient. A two-stage strategy is adopted for offline training and online execution, so the accurate distribution system model is no longer needed. To achieve scalability, a centralized training distributed execution strategy is adopted for a multi-agent framework, which enables a decentralized Volt-VAR control for large-scale distribution system. Comprehensive numerical experiments with real-world electricity data demonstrate that our proposed algorithm can achieve high solution optimality and constraints compliance.
Fine-tuning foundational models to code diagnoses from veterinary health records
Authors: Mayla R. Boguslav, Adam Kiehl, David Kott, G. Joseph Strecker, Tracy Webb, Nadia Saklou, Terri Ward, Michael Kirby
Date: 2024-10-19 00:00:00

Veterinary medical records represent a large data resource for application to veterinary and One Health clinical research efforts. Use of the data is limited by interoperability challenges including inconsistent data formats and data siloing. Clinical coding using standardized medical terminologies enhances the quality of medical records and facilitates their interoperability with veterinary and human health records from other sites. Previous studies, such as DeepTag and VetTag, evaluated the application of Natural Language Processing (NLP) to automate veterinary diagnosis coding, employing long short-term memory (LSTM) and transformer models to infer a subset of Systemized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) diagnosis codes from free-text clinical notes. This study expands on these efforts by incorporating all 7,739 distinct SNOMED-CT diagnosis codes recognized by the Colorado State University (CSU) Veterinary Teaching Hospital (VTH) and by leveraging the increasing availability of pre-trained large language models (LLMs). Ten freely-available pre-trained LLMs were fine-tuned on the free-text notes from 246,473 manually-coded veterinary patient visits included in the CSU VTH's electronic health records (EHRs), which resulted in superior performance relative to previous efforts. The most accurate results were obtained when expansive labeled data were used to fine-tune relatively large clinical LLMs, but the study also showed that comparable results can be obtained using more limited resources and non-clinical LLMs. The results of this study contribute to the improvement of the quality of veterinary EHRs by investigating accessible methods for automated coding and support both animal and human health research by paving the way for more integrated and comprehensive health databases that span species and institutions.