pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Domain-Specific Retrieval-Augmented Generation: Synthetic Data Generation and Evaluation using Reasoning Models
Authors: Aryan Jadon, Avinash Patil, Shashank Kumar
Date: 2025-02-21 00:00:00
Tasks: Concept Alignment, RAG, Retrieval, Retrieval-augmented Generation, Synthetic Data Generation

Retrieval-Augmented Generation (RAG) systems face significant performance gaps when applied to technical domains requiring precise information extraction from complex documents. Current evaluation methodologies relying on document-level metrics inadequately capture token-resolution retrieval accuracy that is critical for domain-related documents. We propose a framework combining granular evaluation metrics with synthetic data generation to optimize domain-specific RAG performance. First, we introduce token-aware metrics Precision $\Omega$ and Intersection-over-Union (IoU) that quantify context preservation versus information density trade-offs inherent in technical texts. Second, we develop a reasoning model-driven pipeline using instruction-tuned LLMs (DeepSeek-R1, DeepSeek-R1 distilled variants, and Phi-4) to generate context-anchored QA pairs with discontinuous reference spans across three specialized corpora: SEC 10-K filings (finance), biomedical abstracts (PubMed), and APT threat reports (cybersecurity). Our empirical analysis reveals critical insights: smaller chunks (less than 10 tokens) improve precision by 31-42% (IoU = 0.071 vs. baseline 0.053) at recall costs (-18%), while domain-specific embedding strategies yield 22% variance in optimal chunk sizing (5-20 tokens). The DeepSeek-R1-Distill-Qwen-32B model demonstrates superior concept alignment (+14% mean IoU over alternatives), though no configuration universally dominates. Financial texts favor larger chunks for risk factor coverage (Recall = 0.81 at size = 20), whereas cybersecurity content benefits from atomic segmentation, Precision $\Omega = 0.28$ at size = 5. Our code is available on https://github.com/aryan-jadon/Synthetic-Data-Generation-and-Evaluation-using-Reasoning-Model
Non-Linear Flow Matching for Full-Atom Peptide Design
Authors: Dengdeng Huang, Shikui Tu
Date: 2025-02-21 00:00:00
Tasks: Diversity, Position

Peptide design plays a pivotal role in therapeutic applications, yet existing AI-assisted methods often struggle to generate stable peptides with high affinity due to their inability to accurately simulate the dynamic docking process. To address this challenge, we propose NLFlow, a novel multi-manifold approach based on non-linear flow matching. Specifically, we design a polynomial-based conditional vector field to accelerate the convergence of the peptide's position towards the target pocket, effectively capturing the temporal inconsistencies across position, rotation, torsion, and amino acid type manifolds. This enables the model to better align with the true conformational changes observed in biological docking processes. Additionally, we incorporate interaction-related information, such as polarity, to enhance the understanding of peptide-protein binding. Extensive experiments demonstrate that NLFlow outperforms existing methods in generating peptides with superior stability, affinity, and diversity, offering a fast and efficient solution for peptide design and advancing the peptide-based therapeutic development.
A Critical Assessment of Modern Generative Models' Ability to Replicate Artistic Styles
Authors: Andrea Asperti, Franky George, Tiberio Marras, Razvan Ciprian Stricescu, Fabio Zanotti
Date: 2025-02-21 00:00:00

In recent years, advancements in generative artificial intelligence have led to the development of sophisticated tools capable of mimicking diverse artistic styles, opening new possibilities for digital creativity and artistic expression. This paper presents a critical assessment of the style replication capabilities of contemporary generative models, evaluating their strengths and limitations across multiple dimensions. We examine how effectively these models reproduce traditional artistic styles while maintaining structural integrity and compositional balance in the generated images. The analysis is based on a new large dataset of AI-generated works imitating artistic styles of the past, holding potential for a wide range of applications: the "AI-pastiche" dataset. The study is supported by extensive user surveys, collecting diverse opinions on the dataset and investigation both technical and aesthetic challenges, including the ability to generate outputs that are realistic and visually convincing, the versatility of models in handling a wide range of artistic styles, and the extent to which they adhere to the content and stylistic specifications outlined in prompts. This paper aims to provide a comprehensive overview of the current state of generative tools in style replication, offering insights into their technical and artistic limitations, potential advancements in model design and training methodologies, and emerging opportunities for enhancing digital artistry, human-AI collaboration, and the broader creative landscape.
PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation
Authors: Tao Fan, Guoqiang Ma, Yuanfeng Song, Lixin Fan, Kai Chen, Qiang Yang
Date: 2025-02-21 00:00:00
Tasks: Knowledge Distillation, Privacy Preserving, Text Generation

Compressing Large Language Models (LLMs) into task-specific Small Language Models (SLMs) encounters two significant challenges: safeguarding domain-specific knowledge privacy and managing limited resources. To tackle these challenges, we propose PPC-GPT, a innovative privacy-preserving federated framework specifically designed for compressing LLMs into task-specific SLMs via pruning and Chain-of-Thought (COT) distillation. PPC-GPT works on a server-client federated architecture, where the client sends differentially private (DP) perturbed task-specific data to the server's LLM. The LLM then generates synthetic data along with their corresponding rationales. This synthetic data is subsequently used for both LLM pruning and retraining processes. Additionally, we harness COT knowledge distillation, leveraging the synthetic data to further improve the retraining of structurally-pruned SLMs. Our experimental results demonstrate the effectiveness of PPC-GPT across various text generation tasks. By compressing LLMs into task-specific SLMs, PPC-GPT not only achieves competitive performance but also prioritizes data privacy protection.
Generative AI Training and Copyright Law
Authors: Tim W. Dornis, Sebastian Stober
Date: 2025-02-21 00:00:00
Tasks: Memorization

Training generative AI models requires extensive amounts of data. A common practice is to collect such data through web scraping. Yet, much of what has been and is collected is copyright protected. Its use may be copyright infringement. In the USA, AI developers rely on "fair use" and in Europe, the prevailing view is that the exception for "Text and Data Mining" (TDM) applies. In a recent interdisciplinary tandem-study, we have argued in detail that this is actually not the case because generative AI training fundamentally differs from TDM. In this article, we share our main findings and the implications for both public and corporate research on generative models. We further discuss how the phenomenon of training data memorization leads to copyright issues independently from the "fair use" and TDM exceptions. Finally, we outline how the ISMIR could contribute to the ongoing discussion about fair practices with respect to generative AI that satisfy all stakeholders.
AI Governance InternationaL Evaluation Index (AGILE Index)
Authors: Yi Zeng, Enmeng Lu, Xin Guan, Cunqing Huangfu, Zizhe Ruan, Ammar Younas, Kang Sun, Xuan Tang, Yuwei Wang, Hongjie Suo, Dongqi Liang, Zhengqiang Han, Aorigele Bao, Xiaoyang Guo, Jin Wang, Jiawei Xie, Yao Liang
Date: 2025-02-21 00:00:00

The rapid advancement of Artificial Intelligence (AI) technology is profoundly transforming human society and concurrently presenting a series of ethical, legal, and social issues. The effective governance of AI has become a crucial global concern. Since 2022, the extensive deployment of generative AI, particularly large language models, marked a new phase in AI governance. Continuous efforts are being made by the international community in actively addressing the novel challenges posed by these AI developments. As consensus on international governance continues to be established and put into action, the practical importance of conducting a global assessment of the state of AI governance is progressively coming to light. In this context, we initiated the development of the AI Governance InternationaL Evaluation Index (AGILE Index). Adhering to the design principle, "the level of governance should match the level of development," the inaugural evaluation of the AGILE Index commences with an exploration of four foundational pillars: the development level of AI, the AI governance environment, the AI governance instruments, and the AI governance effectiveness. It covers 39 indicators across 18 dimensions to comprehensively assess the AI governance level of 14 representative countries globally. The index is utilized to delve into the status of AI governance to date in 14 countries for the first batch of evaluation. The aim is to depict the current state of AI governance in these countries through data scoring, assist them in identifying their governance stage and uncovering governance issues, and ultimately offer insights for the enhancement of their AI governance systems.
Synthetic vs. Gold: The Role of LLM-Generated Labels and Data in Cyberbullying Detection
Authors: Arefeh Kazemi, Sri Balaaji Natarajan Kalaivendan, Joachim Wagner, Hamza Qadeer, Brian Davis
Date: 2025-02-21 00:00:00

This study investigates the role of LLM-generated synthetic data in cyberbullying detection. We conduct a series of experiments where we replace some or all of the authentic data with synthetic data, or augment the authentic data with synthetic data. We find that synthetic cyberbullying data can be the basis for training a classifier for harm detection that reaches performance close to that of a classifier trained with authentic data. Combining authentic with synthetic data shows improvements over the baseline of training on authentic data alone for the test data for all three LLMs tried. These results highlight the viability of synthetic data as a scalable, ethically viable alternative in cyberbullying detection while emphasizing the critical impact of LLM selection on performance outcomes.
Standard Benchmarks Fail -- Auditing LLM Agents in Finance Must Prioritize Risk
Authors: Zichen Chen, Jiaao Chen, Jianda Chen, Misha Sra
Date: 2025-02-21 00:00:00
Tasks: Large Language Model, Position

Standard benchmarks fixate on how well large language model (LLM) agents perform in finance, yet say little about whether they are safe to deploy. We argue that accuracy metrics and return-based scores provide an illusion of reliability, overlooking vulnerabilities such as hallucinated facts, stale data, and adversarial prompt manipulation. We take a firm position: financial LLM agents should be evaluated first and foremost on their risk profile, not on their point-estimate performance. Drawing on risk-engineering principles, we outline a three-level agenda: model, workflow, and system, for stress-testing LLM agents under realistic failure modes. To illustrate why this shift is urgent, we audit six API-based and open-weights LLM agents on three high-impact tasks and uncover hidden weaknesses that conventional benchmarks miss. We conclude with actionable recommendations for researchers, practitioners, and regulators: audit risk-aware metrics in future studies, publish stress scenarios alongside datasets, and treat ``safety budget'' as a primary success criterion. Only by redefining what ``good'' looks like can the community responsibly advance AI-driven finance.
Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence
Authors: Yingying Sun, Jun A, Zhiwei Liu, Rui Sun, Liujia Qian, Samuel H. Payne, Wout Bittremieux, Markus Ralser, Chen Li, Yi Chen, Zhen Dong, Yasset Perez-Riverol, Asif Khan, Chris Sander, Ruedi Aebersold, Juan Antonio Vizcaíno, Jonathan R Krieger, Jianhua Yao, Han Wen, Linfeng Zhang, Yunping Zhu, Yue Xuan, Benjamin Boyang Sun, Liang Qiao, Henning Hermjakob, Haixu Tang, Huanhuan Gao, Yamin Deng, Qing Zhong, Cheng Chang, Nuno Bandeira, Ming Li, Weinan E, Siqi Sun, Yuedong Yang, Gilbert S. Omenn, Yue Zhang, Ping Xu, Yan Fu, Xiaowen Liu, Christopher M. Overall, Yu Wang, Eric W. Deutsch, Luonan Chen, Jürgen Cox, Vadim Demichev, Fuchu He, Jiaxing Huang, Huilin Jin, Chao Liu, Nan Li, Zhongzhi Luan, Jiangning Song, Kaicheng Yu, Wanggen Wan, Tai Wang, Kang Zhang, Le Zhang, Peter A. Bell, Matthias Mann, Bing Zhang, Tiannan Guo
Date: 2025-02-21 00:00:00
Tasks: Diversity

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells.
Generative AI Framework for 3D Object Generation in Augmented Reality
Authors: Majid Behravan
Date: 2025-02-21 00:00:00
Tasks: 3D Generation, object-detection, Object Detection

This thesis presents a framework that integrates state-of-the-art generative AI models for real-time creation of three-dimensional (3D) objects in augmented reality (AR) environments. The primary goal is to convert diverse inputs, such as images and speech, into accurate 3D models, enhancing user interaction and immersion. Key components include advanced object detection algorithms, user-friendly interaction techniques, and robust AI models like Shap-E for 3D generation. Leveraging Vision Language Models (VLMs) and Large Language Models (LLMs), the system captures spatial details from images and processes textual information to generate comprehensive 3D objects, seamlessly integrating virtual objects into real-world environments. The framework demonstrates applications across industries such as gaming, education, retail, and interior design. It allows players to create personalized in-game assets, customers to see products in their environments before purchase, and designers to convert real-world objects into 3D models for real-time visualization. A significant contribution is democratizing 3D model creation, making advanced AI tools accessible to a broader audience, fostering creativity and innovation. The framework addresses challenges like handling multilingual inputs, diverse visual data, and complex environments, improving object detection and model generation accuracy, as well as loading 3D models in AR space in real-time. In conclusion, this thesis integrates generative AI and AR for efficient 3D model generation, enhancing accessibility and paving the way for innovative applications and improved user interactions in AR environments.
Making Sense of AI Limitations: How Individual Perceptions Shape Organizational Readiness for AI Adoption
Authors: Thomas Übellacker
Date: 2025-02-21 00:00:00

This study investigates how individuals' perceptions of artificial intelligence (AI) limitations influence organizational readiness for AI adoption. Through semi-structured interviews with seven AI implementation experts, analyzed using the Gioia methodology, the research reveals that organizational readiness emerges through dynamic interactions between individual sensemaking, social learning, and formal integration processes. The findings demonstrate that hands-on experience with AI limitations leads to more realistic expectations and increased trust, mainly when supported by peer networks and champion systems. Organizations that successfully translate these individual and collective insights into formal governance structures achieve more sustainable AI adoption. The study advances theory by showing how organizational readiness for AI adoption evolves through continuous cycles of individual understanding, social learning, and organizational adaptation. These insights suggest that organizations should approach AI adoption not as a one-time implementation but as an ongoing strategic learning process that balances innovation with practical constraints. The research contributes to organizational readiness theory and practice by illuminating how micro-level perceptions and experiences shape macro-level adoption outcomes.
A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare
Authors: Manar Aljohani, Jun Hou, Sindhura Kommu, Xuan Wang
Date: 2025-02-21 00:00:00
Tasks: Decision Making, Fairness, Misinformation

The application of large language models (LLMs) in healthcare has the potential to revolutionize clinical decision-making, medical research, and patient care. As LLMs are increasingly integrated into healthcare systems, several critical challenges must be addressed to ensure their reliable and ethical deployment. These challenges include truthfulness, where models generate misleading information; privacy, with risks of unintentional data retention; robustness, requiring defenses against adversarial attacks; fairness, addressing biases in clinical outcomes; explainability, ensuring transparent decision-making; and safety, mitigating risks of misinformation and medical errors. Recently, researchers have begun developing benchmarks and evaluation frameworks to systematically assess the trustworthiness of LLMs. However, the trustworthiness of LLMs in healthcare remains underexplored, lacking a systematic review that provides a comprehensive understanding and future insights into this area. This survey bridges this gap by providing a comprehensive overview of the recent research of existing methodologies and solutions aimed at mitigating the above risks in healthcare. By focusing on key trustworthiness dimensions including truthfulness, privacy and safety, robustness, fairness and bias, and explainability, we present a thorough analysis of how these issues impact the reliability and ethical use of LLMs in healthcare. This paper highlights ongoing efforts and offers insights into future research directions to ensure the safe and trustworthy deployment of LLMs in healthcare.
MutaGReP: Execution-Free Repository-Grounded Plan Search for Code-Use
Authors: Zaid Khan, Ali Farhadi, Ranjay Krishna, Luca Weihs, Mohit Bansal, Tanmay Gupta
Date: 2025-02-21 00:00:00
Tasks: Navigate

When a human requests an LLM to complete a coding task using functionality from a large code repository, how do we provide context from the repo to the LLM? One approach is to add the entire repo to the LLM's context window. However, most tasks involve only fraction of symbols from a repo, longer contexts are detrimental to the LLM's reasoning abilities, and context windows are not unlimited. Alternatively, we could emulate the human ability to navigate a large repo, pick out the right functionality, and form a plan to solve the task. We propose MutaGReP (Mutation-guided Grounded Repository Plan Search), an approach to search for plans that decompose a user request into natural language steps grounded in the codebase. MutaGReP performs neural tree search in plan space, exploring by mutating plans and using a symbol retriever for grounding. On the challenging LongCodeArena benchmark, our plans use less than 5% of the 128K context window for GPT-4o but rival the coding performance of GPT-4o with a context window filled with the repo. Plans produced by MutaGReP allow Qwen 2.5 Coder 32B and 72B to match the performance of GPT-4o with full repo context and enable progress on the hardest LongCodeArena tasks. Project page: zaidkhan.me/MutaGReP
Directional Gradient Projection for Robust Fine-Tuning of Foundation Models
Authors: Chengyue Huang, Junjiao Tian, Brisa Maneechotesuwan, Shivang Chopra, Zsolt Kira
Date: 2025-02-21 00:00:00
Tasks: image-classification, Image Classification, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Robust fine-tuning aims to adapt large foundation models to downstream tasks while preserving their robustness to distribution shifts. Existing methods primarily focus on constraining and projecting current model towards the pre-trained initialization based on the magnitudes between fine-tuned and pre-trained weights, which often require extensive hyper-parameter tuning and can sometimes result in underfitting. In this work, we propose Directional Gradient Projection (DiGraP), a novel layer-wise trainable method that incorporates directional information from gradients to bridge regularization and multi-objective optimization. Besides demonstrating our method on image classification, as another contribution we generalize this area to the multi-modal evaluation settings for robust fine-tuning. Specifically, we first bridge the uni-modal and multi-modal gap by performing analysis on Image Classification reformulated Visual Question Answering (VQA) benchmarks and further categorize ten out-of-distribution (OOD) VQA datasets by distribution shift types and degree (i.e. near versus far OOD). Experimental results show that DiGraP consistently outperforms existing baselines across Image Classfication and VQA tasks with discriminative and generative backbones, improving both in-distribution (ID) generalization and OOD robustness.
ML-Driven Approaches to Combat Medicare Fraud: Advances in Class Imbalance Solutions, Feature Engineering, Adaptive Learning, and Business Impact
Authors: Dorsa Farahmandazad, Kasra Danesh
Date: 2025-02-21 00:00:00
Tasks: Diagnostic, Dimensionality Reduction, Feature Engineering, feature selection, Fraud Detection

Medicare fraud poses a substantial challenge to healthcare systems, resulting in significant financial losses and undermining the quality of care provided to legitimate beneficiaries. This study investigates the use of machine learning (ML) to enhance Medicare fraud detection, addressing key challenges such as class imbalance, high-dimensional data, and evolving fraud patterns. A dataset comprising inpatient claims, outpatient claims, and beneficiary details was used to train and evaluate five ML models: Random Forest, KNN, LDA, Decision Tree, and AdaBoost. Data preprocessing techniques included resampling SMOTE method to address the class imbalance, feature selection for dimensionality reduction, and aggregation of diagnostic and procedural codes. Random Forest emerged as the best-performing model, achieving a training accuracy of 99.2% and validation accuracy of 98.8%, and F1-score (98.4%). The Decision Tree also performed well, achieving a validation accuracy of 96.3%. KNN and AdaBoost demonstrated moderate performance, with validation accuracies of 79.2% and 81.1%, respectively, while LDA struggled with a validation accuracy of 63.3% and a low recall of 16.6%. The results highlight the importance of advanced resampling techniques, feature engineering, and adaptive learning in detecting Medicare fraud effectively. This study underscores the potential of machine learning in addressing the complexities of fraud detection. Future work should explore explainable AI and hybrid models to improve interpretability and performance, ensuring scalable and reliable fraud detection systems that protect healthcare resources and beneficiaries.