pwc-archive/papers-with-abstracts huggingface dataset:
The Pitfalls of Growing Group Complexity: LLMs and Social Choice-Based Aggregation for Group Recommendations
Authors: Cedric Waterschoot, Nava Tintarev, Francesco Barile
Date: 2025-05-08 00:00:00
Tasks: In-Context Learning, Recommendation Systems, Zero-Shot Learning

Large Language Models (LLMs) are increasingly applied in recommender systems aimed at both individuals and groups. Previously, Group Recommender Systems (GRS) often used social choice-based aggregation strategies to derive a single recommendation based on the preferences of multiple people. In this paper, we investigate under which conditions language models can perform these strategies correctly based on zero-shot learning and analyse whether the formatting of the group scenario in the prompt affects accuracy. We specifically focused on the impact of group complexity (number of users and items), different LLMs, different prompting conditions, including In-Context learning or generating explanations, and the formatting of group preferences. Our results show that performance starts to deteriorate when considering more than 100 ratings. However, not all language models were equally sensitive to growing group complexity. Additionally, we showed that In-Context Learning (ICL) can significantly increase the performance at higher degrees of group complexity, while adding other prompt modifications, specifying domain cues or prompting for explanations, did not impact accuracy. We conclude that future research should include group complexity as a factor in GRS evaluation due to its effect on LLM performance. Furthermore, we showed that formatting the group scenarios differently, such as rating lists per user or per item, affected accuracy. All in all, our study implies that smaller LLMs are capable of generating group recommendations under the right conditions, making the case for using smaller models that require less computing power and costs.
Latent Preference Coding: Aligning Large Language Models via Discrete Latent Codes
Authors: Zhuocheng Gong, Jian Guan, Wei Wu, Huishuai Zhang, Dongyan Zhao
Date: 2025-05-08 00:00:00

Large language models (LLMs) have achieved remarkable success, yet aligning their generations with human preferences remains a critical challenge. Existing approaches to preference modeling often rely on an explicit or implicit reward function, overlooking the intricate and multifaceted nature of human preferences that may encompass conflicting factors across diverse tasks and populations. To address this limitation, we introduce Latent Preference Coding (LPC), a novel framework that models the implicit factors as well as their combinations behind holistic preferences using discrete latent codes. LPC seamlessly integrates with various offline alignment algorithms, automatically inferring the underlying factors and their importance from data without relying on pre-defined reward functions and hand-crafted combination weights. Extensive experiments on multiple benchmarks demonstrate that LPC consistently improves upon three alignment algorithms (DPO, SimPO, and IPO) using three base models (Mistral-7B, Llama3-8B, and Llama3-8B-Instruct). Furthermore, deeper analysis reveals that the learned latent codes effectively capture the differences in the distribution of human preferences and significantly enhance the robustness of alignment against noise in data. By providing a unified representation for the multifarious preference factors, LPC paves the way towards developing more robust and versatile alignment techniques for the responsible deployment of powerful LLMs.
Rethinking the Relationship between the Power Law and Hierarchical Structures
Authors: Kai Nakaishi, Ryo Yoshida, Kohei Kajikawa, Koji Hukushima, Yohei Oseki
Date: 2025-05-08 00:00:00

Statistical analysis of corpora provides an approach to quantitatively investigate natural languages. This approach has revealed that several power laws consistently emerge across different corpora and languages, suggesting the universal principles underlying languages. Particularly, the power-law decay of correlation has been interpreted as evidence for underlying hierarchical structures in syntax, semantics, and discourse. This perspective has also been extended to child languages and animal signals. However, the argument supporting this interpretation has not been empirically tested. To address this problem, this study examines the validity of the argument for syntactic structures. Specifically, we test whether the statistical properties of parse trees align with the implicit assumptions in the argument. Using English corpora, we analyze the mutual information, deviations from probabilistic context-free grammars (PCFGs), and other properties in parse trees, as well as in the PCFG that approximates these trees. Our results indicate that the assumptions do not hold for syntactic structures and that it is difficult to apply the proposed argument to child languages and animal signals, highlighting the need to reconsider the relationship between the power law and hierarchical structures.
Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendations
Authors: Md Aminul Islam, Ahmed Sayeed Faruk
Date: 2025-05-08 00:00:00
Tasks: Position, Recommendation Systems, Reranking

Recommender systems are essential for delivering personalized content across digital platforms by modeling user preferences and behaviors. Recently, large language models (LLMs) have been adopted for prompt-based recommendation due to their ability to generate personalized outputs without task-specific training. However, LLM-based methods face limitations such as limited context window size, inefficient pointwise and pairwise prompting, and difficulty handling listwise ranking due to token constraints. LLMs can also be sensitive to position bias, as they may overemphasize earlier items in the prompt regardless of their true relevance. To address and investigate these issues, we propose a hybrid framework that combines a traditional recommendation model with an LLM for reranking top-k items using structured prompts. We evaluate the effects of user history reordering and instructional prompts for mitigating position bias. Experiments on MovieLens-100K show that randomizing user history improves ranking quality, but LLM-based reranking does not outperform the base model. Explicit instructions to reduce position bias are also ineffective. Our evaluations reveal limitations in LLMs' ability to model ranking context and mitigate bias. Our code is publicly available at https://github.com/aminul7506/LLMForReRanking.
Unified Attacks to Large Language Model Watermarks: Spoofing and Scrubbing in Unauthorized Knowledge Distillation
Authors: Xin Yi, Yue Li, Shunfan Zheng, LinLin Wang, Xiaoling Wang, Liang He
Date: 2025-04-24 00:00:00
Tasks: Knowledge Distillation, Language Modeling, Language Modelling, Large Language Model, Misinformation

Watermarking has emerged as a critical technique for combating misinformation and protecting intellectual property in large language models (LLMs). A recent discovery, termed watermark radioactivity, reveals that watermarks embedded in teacher models can be inherited by student models through knowledge distillation. On the positive side, this inheritance allows for the detection of unauthorized knowledge distillation by identifying watermark traces in student models. However, the robustness of watermarks against scrubbing attacks and their unforgeability in the face of spoofing attacks under unauthorized knowledge distillation remain largely unexplored. Existing watermark attack methods either assume access to model internals or fail to simultaneously support both scrubbing and spoofing attacks. In this work, we propose Contrastive Decoding-Guided Knowledge Distillation (CDG-KD), a unified framework that enables bidirectional attacks under unauthorized knowledge distillation. Our approach employs contrastive decoding to extract corrupted or amplified watermark texts via comparing outputs from the student model and weakly watermarked references, followed by bidirectional distillation to train new student models capable of watermark removal and watermark forgery, respectively. Extensive experiments show that CDG-KD effectively performs attacks while preserving the general performance of the distilled model. Our findings underscore critical need for developing watermarking schemes that are robust and unforgeable.
Osiris: A Lightweight Open-Source Hallucination Detection System
Authors: Alex Shan, John Bauer, Christopher D. Manning
Date: 2025-05-07 00:00:00
Tasks: Hallucination, RAG, Retrieval-augmented Generation

Retrieval-Augmented Generation (RAG) systems have gained widespread adoption by application builders because they leverage sources of truth to enable Large Language Models (LLMs) to generate more factually sound responses. However, hallucinations, instances of LLM responses that are unfaithful to the provided context, often prevent these systems from being deployed in production environments. Current hallucination detection methods typically involve human evaluation or the use of closed-source models to review RAG system outputs for hallucinations. Both human evaluators and closed-source models suffer from scaling issues due to their high costs and slow inference speeds. In this work, we introduce a perturbed multi-hop QA dataset with induced hallucinations. Via supervised fine-tuning on our dataset, we achieve better recall with a 7B model than GPT-4o on the RAGTruth hallucination detection benchmark and offer competitive performance on precision and accuracy, all while using a fraction of the parameters. Code is released at our repository.
Red Teaming the Mind of the Machine: A Systematic Evaluation of Prompt Injection and Jailbreak Vulnerabilities in LLMs
Authors: Chetan Pathade
Date: 2025-05-07 00:00:00
Tasks: Red Teaming

Large Language Models (LLMs) are increasingly integrated into consumer and enterprise applications. Despite their capabilities, they remain susceptible to adversarial attacks such as prompt injection and jailbreaks that override alignment safeguards. This paper provides a systematic investigation of jailbreak strategies against various state-of-the-art LLMs. We categorize over 1,400 adversarial prompts, analyze their success against GPT-4, Claude 2, Mistral 7B, and Vicuna, and examine their generalizability and construction logic. We further propose layered mitigation strategies and recommend a hybrid red-teaming and sandboxing approach for robust LLM security.
SOAEsV2-7B/72B: Full-Pipeline Optimization for State-Owned Enterprise LLMs via Continual Pre-Training, Domain-Progressive SFT and Distillation-Enhanced Speculative Decoding
Authors: Jingyang Deng, Ran Chen, Jo-Ku Cheng, Jinwen Ma
Date: 2025-05-07 00:00:00

This study addresses key challenges in developing domain-specific large language models (LLMs) for Chinese state-owned assets and enterprises (SOAEs), where current approaches face three limitations: 1) constrained model capacity that limits knowledge integration and cross-task adaptability; 2) excessive reliance on domain-specific supervised fine-tuning (SFT) data, which neglects the broader applicability of general language patterns; and 3) inefficient inference acceleration for large models processing long contexts. In this work, we propose SOAEsV2-7B/72B, a specialized LLM series developed via a three-phase framework: 1) continual pre-training integrates domain knowledge while retaining base capabilities; 2) domain-progressive SFT employs curriculum-based learning strategy, transitioning from weakly relevant conversational data to expert-annotated SOAEs datasets to optimize domain-specific tasks; 3) distillation-enhanced speculative decoding accelerates inference via logit distillation between 72B target and 7B draft models, achieving 1.39-1.52$\times$ speedup without quality loss. Experimental results demonstrate that our domain-specific pre-training phase maintains 99.8% of original general language capabilities while significantly improving domain performance, resulting in a 1.08$\times$ improvement in Rouge-1 score and a 1.17$\times$ enhancement in BLEU-4 score. Ablation studies further show that domain-progressive SFT outperforms single-stage training, achieving 1.02$\times$ improvement in Rouge-1 and 1.06$\times$ in BLEU-4. Our work introduces a comprehensive, full-pipeline approach for optimizing SOAEs LLMs, bridging the gap between general language capabilities and domain-specific expertise.
Integration of Large Language Models and Traditional Deep Learning for Social Determinants of Health Prediction
Authors: Paul Landes, Jimeng Sun, Adam Cross
Date: 2025-05-06 00:00:00
Tasks: Decision Making, Deep Learning

Social Determinants of Health (SDoH) are economic, social and personal circumstances that affect or influence an individual's health status. SDoHs have shown to be correlated to wellness outcomes, and therefore, are useful to physicians in diagnosing diseases and in decision-making. In this work, we automatically extract SDoHs from clinical text using traditional deep learning and Large Language Models (LLMs) to find the advantages and disadvantages of each on an existing publicly available dataset. Our models outperform a previous reference point on a multilabel SDoH classification by 10 points, and we present a method and model to drastically speed up classification (12X execution time) by eliminating expensive LLM processing. The method we present combines a more nimble and efficient solution that leverages the power of the LLM for precision and traditional deep learning methods for efficiency. We also show highly performant results on a dataset supplemented with synthetic data and several traditional deep learning models that outperform LLMs. Our models and methods offer the next iteration of automatic prediction of SDoHs that impact at-risk patients.
A Comparative Analysis of Ethical and Safety Gaps in LLMs using Relative Danger Coefficient
Authors: Yehor Tereshchenko, Mika Hämäläinen
Date: 2025-05-06 00:00:00
Tasks: Natural Language Understanding

Artificial Intelligence (AI) and Large Language Models (LLMs) have rapidly evolved in recent years, showcasing remarkable capabilities in natural language understanding and generation. However, these advancements also raise critical ethical questions regarding safety, potential misuse, discrimination and overall societal impact. This article provides a comparative analysis of the ethical performance of various AI models, including the brand new DeepSeek-V3(R1 with reasoning and without), various GPT variants (4o, 3.5 Turbo, 4 Turbo, o1/o3 mini) and Gemini (1.5 flash, 2.0 flash and 2.0 flash exp) and highlights the need for robust human oversight, especially in situations with high stakes. Furthermore, we present a new metric for calculating harm in LLMs called Relative Danger Coefficient (RDC).
FRAME: Feedback-Refined Agent Methodology for Enhancing Medical Research Insights
Authors: Chengzhang Yu, Yiming Zhang, Zhixin Liu, Zenghui Ding, Yining Sun, Zhanpeng Jin
Date: 2025-05-06 00:00:00
Tasks: Paper generation

The automation of scientific research through large language models (LLMs) presents significant opportunities but faces critical challenges in knowledge synthesis and quality assurance. We introduce Feedback-Refined Agent Methodology (FRAME), a novel framework that enhances medical paper generation through iterative refinement and structured feedback. Our approach comprises three key innovations: (1) A structured dataset construction method that decomposes 4,287 medical papers into essential research components through iterative refinement; (2) A tripartite architecture integrating Generator, Evaluator, and Reflector agents that progressively improve content quality through metric-driven feedback; and (3) A comprehensive evaluation framework that combines statistical metrics with human-grounded benchmarks. Experimental results demonstrate FRAME's effectiveness, achieving significant improvements over conventional approaches across multiple models (9.91% average gain with DeepSeek V3, comparable improvements with GPT-4o Mini) and evaluation dimensions. Human evaluation confirms that FRAME-generated papers achieve quality comparable to human-authored works, with particular strength in synthesizing future research directions. The results demonstrated our work could efficiently assist medical research by building a robust foundation for automated medical research paper generation while maintaining rigorous academic standards.
ChatGPT for automated grading of short answer questions in mechanical ventilation
Authors: Tejas Jade, Alex Yartsev
Date: 2025-05-05 00:00:00

Standardised tests using short answer questions (SAQs) are common in postgraduate education. Large language models (LLMs) simulate conversational language and interpret unstructured free-text responses in ways aligning with applying SAQ grading rubrics, making them attractive for automated grading. We evaluated ChatGPT 4o to grade SAQs in a postgraduate medical setting using data from 215 students (557 short-answer responses) enrolled in an online course on mechanical ventilation (2020--2024). Deidentified responses to three case-based scenarios were presented to ChatGPT with a standardised grading prompt and rubric. Outputs were analysed using mixed-effects modelling, variance component analysis, intraclass correlation coefficients (ICCs), Cohen's kappa, Kendall's W, and Bland--Altman statistics. ChatGPT awarded systematically lower marks than human graders with a mean difference (bias) of -1.34 on a 10-point scale. ICC values indicated poor individual-level agreement (ICC1 = 0.086), and Cohen's kappa (-0.0786) suggested no meaningful agreement. Variance component analysis showed minimal variability among the five ChatGPT sessions (G-value = 0.87), indicating internal consistency but divergence from the human grader. The poorest agreement was observed for evaluative and analytic items, whereas checklist and prescriptive rubric items had less disagreement. We caution against the use of LLMs in grading postgraduate coursework. Over 60% of ChatGPT-assigned grades differed from human grades by more than acceptable boundaries for high-stakes assessments.
Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation
Authors: Hannes Waldetoft, Jakob Torgander, Måns Magnusson
Date: 2025-05-05 00:00:00
Tasks: Survey Sampling

Estimating population parameters in finite populations of text documents can be challenging when obtaining the labels for the target variable requires manual annotation. To address this problem, we combine predictions from a transformer encoder neural network with well-established survey sampling estimators using the model predictions as an auxiliary variable. The applicability is demonstrated in Swedish hate crime statistics based on Swedish police reports. Estimates of the yearly number of hate crimes and the police's under-reporting are derived using the Hansen-Hurwitz estimator, difference estimation, and stratified random sampling estimation. We conclude that if labeled training data is available, the proposed method can provide very efficient estimates with reduced time spent on manual annotation.
Language translation, and change of accent for speech-to-speech task using diffusion model
Authors: Abhishek Mishra, Ritesh Sur Chowdhury, Vartul Bahuguna, Isha Pandey, Ganesh Ramakrishnan
Date: 2025-05-04 00:00:00
Tasks: Speech-to-Speech Translation, Translation

Speech-to-speech translation (S2ST) aims to convert spoken input in one language to spoken output in another, typically focusing on either language translation or accent adaptation. However, effective cross-cultural communication requires handling both aspects simultaneously - translating content while adapting the speaker's accent to match the target language context. In this work, we propose a unified approach for simultaneous speech translation and change of accent, a task that remains underexplored in current literature. Our method reformulates the problem as a conditional generation task, where target speech is generated based on phonemes and guided by target speech features. Leveraging the power of diffusion models, known for high-fidelity generative capabilities, we adapt text-to-image diffusion strategies by conditioning on source speech transcriptions and generating Mel spectrograms representing the target speech with desired linguistic and accentual attributes. This integrated framework enables joint optimization of translation and accent adaptation, offering a more parameter-efficient and effective model compared to traditional pipelines.
Towards Artificial Intelligence Research Assistant for Expert-Involved Learning
Authors: Tianyu Liu, Simeng Han, Xiao Luo, Hanchen Wang, Pan Lu, Biqing Zhu, Yuge Wang, Keyi Li, Jiapeng Chen, Rihao Qu, Yufeng Liu, Xinyue Cui, Aviv Yaish, Yuhang Chen, Minsheng Hao, Chuhan Li, Kexing Li, Arman Cohan, Hua Xu, Mark Gerstein, James Zou, Hongyu Zhao
Date: 2025-05-03 00:00:00
Tasks: Articles, Prompt Engineering

Large Language Models (LLMs) and Large Multi-Modal Models (LMMs) have emerged as transformative tools in scientific research, yet their reliability and specific contributions to biomedical applications remain insufficiently characterized. In this study, we present \textbf{AR}tificial \textbf{I}ntelligence research assistant for \textbf{E}xpert-involved \textbf{L}earning (ARIEL), a multimodal dataset designed to benchmark and enhance two critical capabilities of LLMs and LMMs in biomedical research: summarizing extensive scientific texts and interpreting complex biomedical figures. To facilitate rigorous assessment, we create two open-source sets comprising biomedical articles and figures with designed questions. We systematically benchmark both open- and closed-source foundation models, incorporating expert-driven human evaluations conducted by doctoral-level experts. Furthermore, we improve model performance through targeted prompt engineering and fine-tuning strategies for summarizing research papers, and apply test-time computational scaling to enhance the reasoning capabilities of LMMs, achieving superior accuracy compared to human-expert corrections. We also explore the potential of using LMM Agents to generate scientific hypotheses from diverse multimodal inputs. Overall, our results delineate clear strengths and highlight significant limitations of current foundation models, providing actionable insights and guiding future advancements in deploying large-scale language and multi-modal models within biomedical research.