pwc-archive/papers-with-abstracts huggingface dataset:
Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
Authors: Aoran Gan, Hao Yu, Kai Zhang, Qi Liu, Wenyu Yan, Zhenya Huang, Shiwei Tong, Guoping Hu
Date: 2025-04-21 00:00:00
Tasks: Computational Efficiency, Information Retrieval, RAG, Retrieval, Retrieval-augmented Generation, Survey, Text Generation

Recent advancements in Retrieval-Augmented Generation (RAG) have revolutionized natural language processing by integrating Large Language Models (LLMs) with external information retrieval, enabling accurate, up-to-date, and verifiable text generation across diverse applications. However, evaluating RAG systems presents unique challenges due to their hybrid architecture that combines retrieval and generation components, as well as their dependence on dynamic knowledge sources in the LLM era. In response, this paper provides a comprehensive survey of RAG evaluation methods and frameworks, systematically reviewing traditional and emerging evaluation approaches, for system performance, factual accuracy, safety, and computational efficiency in the LLM era. We also compile and categorize the RAG-specific datasets and evaluation frameworks, conducting a meta-analysis of evaluation practices in high-impact RAG research. To the best of our knowledge, this work represents the most comprehensive survey for RAG evaluation, bridging traditional and LLM-driven methods, and serves as a critical resource for advancing RAG development.
Natural Fingerprints of Large Language Models
Authors: Teppei Suzuki, Ryokan Ri, Sho Takase
Date: 2025-04-21 00:00:00

Large language models (LLMs) often exhibit biases -- systematic deviations from expected norms -- in their outputs. These range from overt issues, such as unfair responses, to subtler patterns that can reveal which model produced them. We investigate the factors that give rise to identifiable characteristics in LLMs. Since LLMs model training data distribution, it is reasonable that differences in training data naturally lead to the characteristics. However, our findings reveal that even when LLMs are trained on the exact same data, it is still possible to distinguish the source model based on its generated text. We refer to these unintended, distinctive characteristics as natural fingerprints. By systematically controlling training conditions, we show that the natural fingerprints can emerge from subtle differences in the training process, such as parameter sizes, optimization settings, and even random seeds. We believe that understanding natural fingerprints offers new insights into the origins of unintended bias and ways for improving control over LLM behavior.
Transparentize the Internal and External Knowledge Utilization in LLMs with Trustworthy Citation
Authors: Jiajun Shen, Tong Zhou, Yubo Chen, Delai Qiu, Shengping Liu, Kang Liu, Jun Zhao
Date: 2025-04-21 00:00:00
Tasks: Retrieval, Retrieval-augmented Generation

While hallucinations of large language models could been alleviated through retrieval-augmented generation and citation generation, how the model utilizes internal knowledge is still opaque, and the trustworthiness of its generated answers remains questionable. In this work, we introduce Context-Prior Augmented Citation Generation task, requiring models to generate citations considering both external and internal knowledge while providing trustworthy references, with 5 evaluation metrics focusing on 3 aspects: answer helpfulness, citation faithfulness, and trustworthiness. We introduce RAEL, the paradigm for our task, and also design INTRALIGN, an integrated method containing customary data generation and an alignment algorithm. Our experimental results show that our method achieves a better cross-scenario performance with regard to other baselines. Our extended experiments further reveal that retrieval quality, question types, and model knowledge have considerable influence on the trustworthiness in citation generation.
Completing A Systematic Review in Hours instead of Months with Interactive AI Agents
Authors: Rui Qiu, Shijie Chen, Yu Su, Po-Yin Yen, Han-Wei Shen
Date: 2025-04-21 00:00:00
Tasks: AI Agent

Systematic reviews (SRs) are vital for evidence-based practice in high stakes disciplines, such as healthcare, but are often impeded by intensive labors and lengthy processes that can take months to complete. Due to the high demand for domain expertise, existing automatic summarization methods fail to accurately identify relevant studies and generate high-quality summaries. To that end, we introduce InsightAgent, a human-centered interactive AI agent powered by large language models that revolutionize this workflow. InsightAgent partitions a large literature corpus based on semantics and employs a multi-agent design for more focused processing of literature, leading to significant improvement in the quality of generated SRs. InsightAgent also provides intuitive visualizations of the corpus and agent trajectories, allowing users to effortlessly monitor the actions of the agent and provide real-time feedback based on their expertise. Our user studies with 9 medical professionals demonstrate that the visualization and interaction mechanisms can effectively improve the quality of synthesized SRs by 27.2%, reaching 79.7% of human-written quality. At the same time, user satisfaction is improved by 34.4%. With InsightAgent, it only takes a clinician about 1.5 hours, rather than months, to complete a high-quality systematic review.
Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings
Authors: Saniya Karwa, Navpreet Singh
Date: 2025-04-20 00:00:00
Tasks: Negation, Sentence

Understanding the inner workings of neural embeddings, particularly in models such as BERT, remains a challenge because of their high-dimensional and opaque nature. This paper proposes a framework for uncovering the specific dimensions of vector embeddings that encode distinct linguistic properties (LPs). We introduce the Linguistically Distinct Sentence Pairs (LDSP-10) dataset, which isolates ten key linguistic features such as synonymy, negation, tense, and quantity. Using this dataset, we analyze BERT embeddings with various methods, including the Wilcoxon signed-rank test, mutual information, and recursive feature elimination, to identify the most influential dimensions for each LP. We introduce a new metric, the Embedding Dimension Impact (EDI) score, which quantifies the relevance of each embedding dimension to a LP. Our findings show that certain properties, such as negation and polarity, are robustly encoded in specific dimensions, while others, like synonymy, exhibit more complex patterns. This study provides insights into the interpretability of embeddings, which can guide the development of more transparent and optimized language models, with implications for model bias mitigation and the responsible deployment of AI systems.
PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines
Authors: Reya Vir, Shreya Shankar, Harrison Chase, Will Fu-Hinthorn, Aditya Parameswaran
Date: 2025-04-20 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Marketing, Prompt Engineering

Large language models (LLMs) are increasingly deployed in specialized production data processing pipelines across diverse domains -- such as finance, marketing, and e-commerce. However, when running them in production across many inputs, they often fail to follow instructions or meet developer expectations. To improve reliability in these applications, creating assertions or guardrails for LLM outputs to run alongside the pipelines is essential. Yet, determining the right set of assertions that capture developer requirements for a task is challenging. In this paper, we introduce PROMPTEVALS, a dataset of 2087 LLM pipeline prompts with 12623 corresponding assertion criteria, sourced from developers using our open-source LLM pipeline tools. This dataset is 5x larger than previous collections. Using a hold-out test split of PROMPTEVALS as a benchmark, we evaluated closed- and open-source models in generating relevant assertions. Notably, our fine-tuned Mistral and Llama 3 models outperform GPT-4o by 20.93% on average, offering both reduced latency and improved performance. We believe our dataset can spur further research in LLM reliability, alignment, and prompt engineering.
Evaluating BERTopic on Open-Ended Data: A Case Study with Belgian Dutch Daily Narratives
Authors: Ratna Kandala, Katie Hoemann
Date: 2025-04-20 00:00:00

This study explores BERTopic's potential for modeling open-ended Belgian Dutch daily narratives, contrasting its performance with Latent Dirichlet Allocation (LDA) and KMeans. Although LDA scores well on certain automated metrics, human evaluations reveal semantically irrelevant co-occurrences, highlighting the limitations of purely statistic-based methods. In contrast, BERTopic's reliance on contextual embeddings yields culturally resonant themes, underscoring the importance of hybrid evaluation frameworks that account for morphologically rich languages. KMeans performed less coherently than prior research suggested, pointing to the unique challenges posed by personal narratives. Our findings emphasize the need for robust generalization in NLP models, especially in underrepresented linguistic contexts.
OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding
Authors: Songtao Jiang, YuAn Wang, Sibo Song, Yan Zhang, Zijie Meng, Bohan Lei, Jian Wu, Jimeng Sun, Zuozhu Liu
Date: 2025-04-20 00:00:00
Tasks: Language Modeling, Language Modelling, Video Understanding

The practical deployment of medical vision-language models (Med-VLMs) necessitates seamless integration of textual data with diverse visual modalities, including 2D/3D images and videos, yet existing models typically employ separate encoders for different modalities. To address this limitation, we present OmniV-Med, a unified framework for multimodal medical understanding. Our technical contributions are threefold: First, we construct OmniV-Med-Instruct, a comprehensive multimodal medical dataset containing 252K instructional samples spanning 14 medical image modalities and 11 clinical tasks. Second, we devise a rotary position-adaptive encoder that processes multi-resolution 2D/3D images and videos within a unified architecture, diverging from conventional modality-specific encoders. Third, we introduce a medical-aware token pruning mechanism that exploits spatial-temporal redundancy in volumetric data (e.g., consecutive CT slices) and medical videos, effectively reducing 60\% of visual tokens without performance degradation. Empirical evaluations demonstrate that OmniV-Med-7B achieves state-of-the-art performance on 7 benchmarks spanning 2D/3D medical imaging and video understanding tasks. Notably, our lightweight variant (OmniV-Med-1.5B) attains comparable performance while requiring only 8 RTX3090 GPUs for training and supporting efficient long-video inference. Data, code and model will be released.
Trans-Zero: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data
Authors: Wei Zou, Sen yang, Yu Bao, ShuJian Huang, Jiajun Chen, Shanbo Cheng
Date: 2025-04-20 00:00:00
Tasks: Machine Translation, Translation

The rise of Large Language Models (LLMs) has reshaped machine translation (MT), but multilingual MT still relies heavily on parallel data for supervised fine-tuning (SFT), facing challenges like data scarcity for low-resource languages and catastrophic forgetting. To address these issues, we propose TRANS-ZERO, a self-play framework that leverages only monolingual data and the intrinsic multilingual knowledge of LLM. TRANS-ZERO combines Genetic Monte-Carlo Tree Search (G-MCTS) with preference optimization, achieving strong translation performance that rivals supervised methods. Experiments demonstrate that this approach not only matches the performance of models trained on large-scale parallel data but also excels in non-English translation directions. Further analysis reveals that G-MCTS itself significantly enhances translation quality by exploring semantically consistent candidates through iterative translations, providing a robust foundation for the framework's succuss.
Harnessing Generative LLMs for Enhanced Financial Event Entity Extraction Performance
Authors: Soo-joon Choi, Ji-jun Park
Date: 2025-04-20 00:00:00
Tasks: Knowledge Graphs, parameter-efficient fine-tuning, Structured Output Generation

Financial event entity extraction is a crucial task for analyzing market dynamics and building financial knowledge graphs, yet it presents significant challenges due to the specialized language and complex structures in financial texts. Traditional approaches often rely on sequence labeling models, which can struggle with long-range dependencies and the inherent complexity of extracting multiple, potentially overlapping entities. Motivated by the advanced language understanding and generative capabilities of Large Language Models (LLMs), we propose a novel method that reframes financial event entity extraction as a text-to-structured-output generation task. Our approach involves fine-tuning a pre-trained LLM using Parameter-Efficient Fine-Tuning (PEFT) to directly generate a structured representation, such as a JSON object, containing the extracted entities and their precise character spans from the input text. We evaluate our method on the challenging CCKS 2019 Financial Event Entity Extraction dataset, comparing its performance against strong sequence labeling baselines, including SEBERTNets and sebertNets. Experimental results demonstrate that our generative LLM method achieves a new state-of-the-art F1 score on this benchmark, significantly outperforming previous methods. Through detailed quantitative analysis across event types, entity types, and instance complexity, as well as human evaluation, we show that our approach is more effective at handling the nuances of financial text and extracting high-quality entities. This work validates the potential of applying generative LLMs directly to complex, domain-specific information extraction tasks requiring structured output.
Automatic Text Summarization (ATS) for Research Documents in Sorani Kurdish
Authors: Rondik Hadi Abdulrahman, Hossein Hassani
Date: 2025-04-20 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence, Text Summarization

Extracting concise information from scientific documents aids learners, researchers, and practitioners. Automatic Text Summarization (ATS), a key Natural Language Processing (NLP) application, automates this process. While ATS methods exist for many languages, Kurdish remains underdeveloped due to limited resources. This study develops a dataset and language model based on 231 scientific papers in Sorani Kurdish, collected from four academic departments in two universities in the Kurdistan Region of Iraq (KRI), averaging 26 pages per document. Using Sentence Weighting and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms, two experiments were conducted, differing in whether the conclusions were included. The average word count was 5,492.3 in the first experiment and 5,266.96 in the second. Results were evaluated manually and automatically using ROUGE-1, ROUGE-2, and ROUGE-L metrics, with the best accuracy reaching 19.58%. Six experts conducted manual evaluations using three criteria, with results varying by document. This research provides valuable resources for Kurdish NLP researchers to advance ATS and related fields.
A Hierarchical Framework for Measuring Scientific Paper Innovation via Large Language Models
Authors: Hongming Tan, Shaoxiong Zhan, Fengwei Jia, Hai-Tao Zheng, Wai Kin Chan
Date: 2025-04-20 00:00:00
Tasks: Question Answering

Measuring scientific paper innovation is both important and challenging. Existing content-based methods often overlook the full-paper context, fail to capture the full scope of innovation, and lack generalization. We propose HSPIM, a hierarchical and training-free framework based on large language models (LLMs). It introduces a Paper-to-Sections-to-QAs decomposition to assess innovation. We segment the text by section titles and use zero-shot LLM prompting to implement section classification, question-answering (QA) augmentation, and weighted novelty scoring. The generated QA pair focuses on section-level innovation and serves as additional context to improve the LLM scoring. For each chunk, the LLM outputs a novelty score and a confidence score. We use confidence scores as weights to aggregate novelty scores into a paper-level innovation score. To further improve performance, we propose a two-layer question structure consisting of common and section-specific questions, and apply a genetic algorithm to optimize the question-prompt combinations. Comprehensive experiments on scientific conference paper datasets show that HSPIM outperforms baseline methods in effectiveness, generalization, and interpretability.
Translation Analytics for Freelancers: I. Introduction, Data Preparation, Baseline Evaluations
Authors: Yuri Balashov, Alex Balashov, Shiho Fukuda Koski
Date: 2025-04-20 00:00:00
Tasks: Machine Translation, Translation

This is the first in a series of papers exploring the rapidly expanding new opportunities arising from recent progress in language technologies for individual translators and language service providers with modest resources. The advent of advanced neural machine translation systems, large language models, and their integration into workflows via computer-assisted translation tools and translation management systems have reshaped the translation landscape. These advancements enable not only translation but also quality evaluation, error spotting, glossary generation, and adaptation to domain-specific needs, creating new technical opportunities for freelancers. In this series, we aim to empower translators with actionable methods to harness these advancements. Our approach emphasizes Translation Analytics, a suite of evaluation techniques traditionally reserved for large-scale industry applications but now becoming increasingly available for smaller-scale users. This first paper introduces a practical framework for adapting automatic evaluation metrics -- such as BLEU, chrF, TER, and COMET -- to freelancers' needs. We illustrate the potential of these metrics using a trilingual corpus derived from a real-world project in the medical domain and provide statistical analysis correlating human evaluations with automatic scores. Our findings emphasize the importance of proactive engagement with emerging technologies to not only adapt but thrive in the evolving professional environment.
a1: Steep Test-time Scaling Law via Environment Augmented Generation
Authors: Lingrui Mei, Shenghua Liu, Yiwei Wang, Baolong Bi, Yuyao Ge, Jun Wan, Yurong Wu, Xueqi Cheng
Date: 2025-04-20 00:00:00

Large Language Models (LLMs) have made remarkable breakthroughs in reasoning, yet continue to struggle with hallucinations, logical errors, and inability to self-correct during complex multi-step tasks. Current approaches like chain-of-thought prompting offer limited reasoning capabilities that fail when precise step validation is required. We propose Environment Augmented Generation (EAG), a framework that enhances LLM reasoning through: (1) real-time environmental feedback validating each reasoning step, (2) dynamic branch exploration for investigating alternative solution paths when faced with errors, and (3) experience-based learning from successful reasoning trajectories. Unlike existing methods, EAG enables deliberate backtracking and strategic replanning through tight integration of execution feedback with branching exploration. Our a1-32B model achieves state-of-the-art performance among similar-sized models across all benchmarks, matching larger models like o1 on competition mathematics while outperforming comparable models by up to 24.4 percentage points. Analysis reveals EAG's distinctive scaling pattern: initial token investment in environment interaction yields substantial long-term performance dividends, with advantages amplifying proportionally to task complexity. EAG's theoretical framework demonstrates how environment interactivity and systematic branch exploration together establish a new paradigm for reliable machine reasoning, particularly for problems requiring precise multi-step calculation and logical verification.
BookWorld: From Novels to Interactive Agent Societies for Creative Story Generation
Authors: Yiting Ran, Xintao Wang, Tian Qiu, Jiaqing Liang, Yanghua Xiao, Deqing Yang
Date: 2025-04-20 00:00:00
Tasks: Story Generation

Recent advances in large language models (LLMs) have enabled social simulation through multi-agent systems. Prior efforts focus on agent societies created from scratch, assigning agents with newly defined personas. However, simulating established fictional worlds and characters remain largely underexplored, despite its significant practical value. In this paper, we introduce BookWorld, a comprehensive system for constructing and simulating book-based multi-agent societies. BookWorld's design covers comprehensive real-world intricacies, including diverse and dynamic characters, fictional worldviews, geographical constraints and changes, e.t.c. BookWorld enables diverse applications including story generation, interactive games and social simulation, offering novel ways to extend and explore beloved fictional works. Through extensive experiments, we demonstrate that BookWorld generates creative, high-quality stories while maintaining fidelity to the source books, surpassing previous methods with a win rate of 75.36%. The code of this paper can be found at the project page: https://bookworld2025.github.io/.
Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey
Authors: Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer, Muhammad Awais Khan Bangash, Muhammad Ali Jamshed
Date: 2025-04-20 00:00:00
Tasks: Multi-agent Reinforcement Learning, Survey

This survey explores the development of meta-thinking capabilities in Large Language Models (LLMs) from a Multi-Agent Reinforcement Learning (MARL) perspective. Meta-thinking self-reflection, assessment, and control of thinking processes is an important next step in enhancing LLM reliability, flexibility, and performance, particularly for complex or high-stakes tasks. The survey begins by analyzing current LLM limitations, such as hallucinations and the lack of internal self-assessment mechanisms. It then talks about newer methods, including RL from human feedback (RLHF), self-distillation, and chain-of-thought prompting, and each of their limitations. The crux of the survey is to talk about how multi-agent architectures, namely supervisor-agent hierarchies, agent debates, and theory of mind frameworks, can emulate human-like introspective behavior and enhance LLM robustness. By exploring reward mechanisms, self-play, and continuous learning methods in MARL, this survey gives a comprehensive roadmap to building introspective, adaptive, and trustworthy LLMs. Evaluation metrics, datasets, and future research avenues, including neuroscience-inspired architectures and hybrid symbolic reasoning, are also discussed.