pwc-archive/papers-with-abstracts huggingface dataset:
Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes
Authors: Mohammad Aqib, Mohd Hamza, Qipei Mei, Ying Hei Chui
Date: 2025-05-07 00:00:00
Tasks: Language Modeling, Language Modelling, Navigate, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Building codes are regulations that establish standards for the design, construction, and safety of buildings to ensure structural integrity, fire protection, and accessibility. They are often extensive, complex, and subject to frequent updates, making manual querying challenging and time-consuming. Key difficulties include navigating large volumes of text, interpreting technical language, and identifying relevant clauses across different sections. A potential solution is to build a Question-Answering (QA) system that answers user queries based on building codes. Among the various methods for building a QA system, Retrieval-Augmented Generation (RAG) stands out in performance. RAG consists of two components: a retriever and a language model. This study focuses on identifying a suitable retriever method for building codes and optimizing the generational capability of the language model using fine-tuning techniques. We conducted a detailed evaluation of various retrieval methods by performing the retrieval on the National Building Code of Canada (NBCC) and explored the impact of domain-specific fine-tuning on several language models using the dataset derived from NBCC. Our analysis included a comparative assessment of different retrievers and the performance of both pre-trained and fine-tuned models to determine the efficacy and domain-specific adaptation of language models using fine-tuning on the NBCC dataset. Experimental results showed that Elasticsearch proved to be the most robust retriever among all. The findings also indicate that fine-tuning language models on an NBCC-specific dataset can enhance their ability to generate contextually relevant responses. When combined with context retrieved by a powerful retriever like Elasticsearch, this improvement in LLM performance can optimize the RAG system, enabling it to better navigate the complexities of the NBCC.
Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions
Authors: Adithya Kulkarni, Fatimah Alotaibi, Xinyue Zeng, Longfeng Wu, Tong Zeng, Barry Menglong Yao, Minqian Liu, Shuaicheng Zhang, Lifu Huang, Dawei Zhou
Date: 2025-05-06 00:00:00
Tasks: Causal Inference, Domain Adaptation, In-Context Learning, Knowledge Graph Completion, Retrieval, Retrieval-augmented Generation, scientific discovery, Survey, Uncertainty Quantification

Large Language Models (LLMs) are transforming scientific hypothesis generation and validation by enabling information synthesis, latent relationship discovery, and reasoning augmentation. This survey provides a structured overview of LLM-driven approaches, including symbolic frameworks, generative models, hybrid systems, and multi-agent architectures. We examine techniques such as retrieval-augmented generation, knowledge-graph completion, simulation, causal inference, and tool-assisted reasoning, highlighting trade-offs in interpretability, novelty, and domain alignment. We contrast early symbolic discovery systems (e.g., BACON, KEKADA) with modern LLM pipelines that leverage in-context learning and domain adaptation via fine-tuning, retrieval, and symbolic grounding. For validation, we review simulation, human-AI collaboration, causal modeling, and uncertainty quantification, emphasizing iterative assessment in open-world contexts. The survey maps datasets across biomedicine, materials science, environmental science, and social science, introducing new resources like AHTech and CSKG-600. Finally, we outline a roadmap emphasizing novelty-aware generation, multimodal-symbolic integration, human-in-the-loop systems, and ethical safeguards, positioning LLMs as agents for principled, scalable scientific discovery.
Quantum QSAR for drug discovery
Authors: Alejandro Giraldo, Daniel Ruiz, Mariano Caruso, Guido Bellomo
Date: 2025-05-06 00:00:00
Tasks: Drug Discovery

Quantitative Structure-Activity Relationship (QSAR) modeling is key in drug discovery, but classical methods face limitations when handling high-dimensional data and capturing complex molecular interactions. This research proposes enhancing QSAR techniques through Quantum Support Vector Machines (QSVMs), which leverage quantum computing principles to process information Hilbert spaces. By using quantum data encoding and quantum kernel functions, we aim to develop more accurate and efficient predictive models.
Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging
Authors: Shiqi Chen, Jinghan Zhang, Tongyao Zhu, Wei Liu, Siyang Gao, Miao Xiong, Manling Li, Junxian He
Date: 2025-05-08 00:00:00

Vision-Language Models (VLMs) combine visual perception with the general capabilities, such as reasoning, of Large Language Models (LLMs). However, the mechanisms by which these two abilities can be combined and contribute remain poorly understood. In this work, we explore to compose perception and reasoning through model merging that connects parameters of different models. Unlike previous works that often focus on merging models of the same kind, we propose merging models across modalities, enabling the incorporation of the reasoning capabilities of LLMs into VLMs. Through extensive experiments, we demonstrate that model merging offers a successful pathway to transfer reasoning abilities from LLMs to VLMs in a training-free manner. Moreover, we utilize the merged models to understand the internal mechanism of perception and reasoning and how merging affects it. We find that perception capabilities are predominantly encoded in the early layers of the model, whereas reasoning is largely facilitated by the middle-to-late layers. After merging, we observe that all layers begin to contribute to reasoning, whereas the distribution of perception abilities across layers remains largely unchanged. These observations shed light on the potential of model merging as a tool for multimodal integration and interpretation.
UKElectionNarratives: A Dataset of Misleading Narratives Surrounding Recent UK General Elections
Authors: Fatima Haouari, Carolina Scarton, Nicolò Faggiani, Nikolaos Nikolaidis, Bonka Kotseva, Ibrahim Abu Farha, Jens Linge, Kalina Bontcheva
Date: 2025-05-08 00:00:00

Misleading narratives play a crucial role in shaping public opinion during elections, as they can influence how voters perceive candidates and political parties. This entails the need to detect these narratives accurately. To address this, we introduce the first taxonomy of common misleading narratives that circulated during recent elections in Europe. Based on this taxonomy, we construct and analyse UKElectionNarratives: the first dataset of human-annotated misleading narratives which circulated during the UK General Elections in 2019 and 2024. We also benchmark Pre-trained and Large Language Models (focusing on GPT-4o), studying their effectiveness in detecting election-related misleading narratives. Finally, we discuss potential use cases and make recommendations for future research directions using the proposed codebook and dataset.
clem:todd: A Framework for the Systematic Benchmarking of LLM-Based Task-Oriented Dialogue System Realisations
Authors: Chalamalasetti Kranti, Sherzod Hakimov, David Schlangen
Date: 2025-05-08 00:00:00
Tasks: Benchmarking, Task-Oriented Dialogue Systems

The emergence of instruction-tuned large language models (LLMs) has advanced the field of dialogue systems, enabling both realistic user simulations and robust multi-turn conversational agents. However, existing research often evaluates these components in isolation-either focusing on a single user simulator or a specific system design-limiting the generalisability of insights across architectures and configurations. In this work, we propose clem todd (chat-optimized LLMs for task-oriented dialogue systems development), a flexible framework for systematically evaluating dialogue systems under consistent conditions. clem todd enables detailed benchmarking across combinations of user simulators and dialogue systems, whether existing models from literature or newly developed ones. It supports plug-and-play integration and ensures uniform datasets, evaluation metrics, and computational constraints. We showcase clem todd's flexibility by re-evaluating existing task-oriented dialogue systems within this unified setup and integrating three newly proposed dialogue systems into the same evaluation pipeline. Our results provide actionable insights into how architecture, scale, and prompting strategies affect dialogue performance, offering practical guidance for building efficient and effective conversational AI systems.
Ultra-FineWeb: Efficient Data Filtering and Verification for High-Quality LLM Training Data
Authors: Yudong Wang, Zixuan Fu, Jie Cai, Peijun Tang, Hongya Lyu, Yewei Fang, Zhi Zheng, Jie zhou, Guoyang Zeng, Chaojun Xiao, Xu Han, Zhiyuan Liu
Date: 2025-05-08 00:00:00

Data quality has become a key factor in enhancing model performance with the rapid development of large language models (LLMs). Model-driven data filtering has increasingly become a primary approach for acquiring high-quality data. However, it still faces two main challenges: (1) the lack of an efficient data verification strategy makes it difficult to provide timely feedback on data quality; and (2) the selection of seed data for training classifiers lacks clear criteria and relies heavily on human expertise, introducing a degree of subjectivity. To address the first challenge, we introduce an efficient verification strategy that enables rapid evaluation of the impact of data on LLM training with minimal computational cost. To tackle the second challenge, we build upon the assumption that high-quality seed data is beneficial for LLM training, and by integrating the proposed verification strategy, we optimize the selection of positive and negative samples and propose an efficient data filtering pipeline. This pipeline not only improves filtering efficiency, classifier quality, and robustness, but also significantly reduces experimental and inference costs. In addition, to efficiently filter high-quality data, we employ a lightweight classifier based on fastText, and successfully apply the filtering pipeline to two widely-used pre-training corpora, FineWeb and Chinese FineWeb datasets, resulting in the creation of the higher-quality Ultra-FineWeb dataset. Ultra-FineWeb contains approximately 1 trillion English tokens and 120 billion Chinese tokens. Empirical results demonstrate that the LLMs trained on Ultra-FineWeb exhibit significant performance improvements across multiple benchmark tasks, validating the effectiveness of our pipeline in enhancing both data quality and training efficiency.
Frame In, Frame Out: Do LLMs Generate More Biased News Headlines than Humans?
Authors: Valeria Pastorino, Nafise Sadat Moosavi
Date: 2025-05-08 00:00:00

Framing in media critically shapes public perception by selectively emphasizing some details while downplaying others. With the rise of large language models in automated news and content creation, there is growing concern that these systems may introduce or even amplify framing biases compared to human authors. In this paper, we explore how framing manifests in both out-of-the-box and fine-tuned LLM-generated news content. Our analysis reveals that, particularly in politically and socially sensitive contexts, LLMs tend to exhibit more pronounced framing than their human counterparts. In addition, we observe significant variation in framing tendencies across different model architectures, with some models displaying notably higher biases. These findings point to the need for effective post-training mitigation strategies and tighter evaluation frameworks to ensure that automated news content upholds the standards of balanced reporting.
RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection
Authors: Yixin Yang, Qingxiu Dong, Linli Yao, Fangwei Zhu, Zhifang Sui
Date: 2025-05-08 00:00:00
Tasks: In-Context Learning, Inductive Bias

Data selection for instruction tuning is crucial for improving the performance of large language models (LLMs) while reducing training costs. In this paper, we propose Refined Contribution Measurement with In-Context Learning (RICo), a novel gradient-free method that quantifies the fine-grained contribution of individual samples to both task-level and global-level model performance. RICo enables more accurate identification of high-contribution data, leading to better instruction tuning. We further introduce a lightweight selection paradigm trained on RICo scores, enabling scalable data selection with a strictly linear inference complexity. Extensive experiments on three LLMs across 12 benchmarks and 5 pairwise evaluation sets demonstrate the effectiveness of RICo. Remarkably, on LLaMA3.1-8B, models trained on 15% of RICo-selected data outperform full datasets by 5.42% points and exceed the best performance of widely used selection methods by 2.06% points. We further analyze high-contribution samples selected by RICo, which show both diverse tasks and appropriate difficulty levels, rather than just the hardest ones.
Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design
Authors: Elena Musi, Nadin Kokciyan, Khalid Al-Khatib, Davide Ceolin, Emmanuelle Dietz, Klara Gutekunst, Annette Hautli-Janisz, Cristian Manuel Santibañez Yañez, Jodi Schneider, Jonas Scholz, Cor Steging, Jacky Visser, Henning Wachsmuth
Date: 2025-05-08 00:00:00
Tasks: Position

In this position paper, we advocate for the development of conversational technology that is inherently designed to support and facilitate argumentative processes. We argue that, at present, large language models (LLMs) are inadequate for this purpose, and we propose an ideal technology design aimed at enhancing argumentative skills. This involves re-framing LLMs as tools to exercise our critical thinking skills rather than replacing them. We introduce the concept of \textit{reasonable parrots} that embody the fundamental principles of relevance, responsibility, and freedom, and that interact through argumentative dialogical moves. These principles and moves arise out of millennia of work in argumentation theory and should serve as the starting point for LLM-based technology that incorporates basic principles of argumentation.
QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation
Authors: Mengze Hong, Wailing Ng, Di Jiang, Chen Jason Zhang
Date: 2025-05-08 00:00:00
Tasks: Benchmarking, Federated Learning, RAG

The rapid advancement of Chinese large language models (LLMs) underscores the need for domain-specific evaluations to ensure reliable applications. However, existing benchmarks often lack coverage in vertical domains and offer limited insights into the Chinese working context. Leveraging qualification exams as a unified framework for human expertise evaluation, we introduce QualBench, the first multi-domain Chinese QA benchmark dedicated to localized assessment of Chinese LLMs. The dataset includes over 17,000 questions across six vertical domains, with data selections grounded in 24 Chinese qualifications to closely align with national policies and working standards. Through comprehensive evaluation, the Qwen2.5 model outperformed the more advanced GPT-4o, with Chinese LLMs consistently surpassing non-Chinese models, highlighting the importance of localized domain knowledge in meeting qualification requirements. The best performance of 75.26% reveals the current gaps in domain coverage within model capabilities. Furthermore, we present the failure of LLM collaboration with crowdsourcing mechanisms and suggest the opportunities for multi-domain RAG knowledge enhancement and vertical domain LLM training with Federated Learning.
A Benchmark Dataset and a Framework for Urdu Multimodal Named Entity Recognition
Authors: Hussain Ahmad, Qingyang Zeng, Jing Wan
Date: 2025-05-08 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition

The emergence of multimodal content, particularly text and images on social media, has positioned Multimodal Named Entity Recognition (MNER) as an increasingly important area of research within Natural Language Processing. Despite progress in high-resource languages such as English, MNER remains underexplored for low-resource languages like Urdu. The primary challenges include the scarcity of annotated multimodal datasets and the lack of standardized baselines. To address these challenges, we introduce the U-MNER framework and release the Twitter2015-Urdu dataset, a pioneering resource for Urdu MNER. Adapted from the widely used Twitter2015 dataset, it is annotated with Urdu-specific grammar rules. We establish benchmark baselines by evaluating both text-based and multimodal models on this dataset, providing comparative analyses to support future research on Urdu MNER. The U-MNER framework integrates textual and visual context using Urdu-BERT for text embeddings and ResNet for visual feature extraction, with a Cross-Modal Fusion Module to align and fuse information. Our model achieves state-of-the-art performance on the Twitter2015-Urdu dataset, laying the groundwork for further MNER research in low-resource languages.
Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders
Authors: Boyi Deng, Yu Wan, Yidan Zhang, Baosong Yang, Fuli Feng
Date: 2025-05-08 00:00:00

The mechanisms behind multilingual capabilities in Large Language Models (LLMs) have been examined using neuron-based or internal-activation-based methods. However, these methods often face challenges such as superposition and layer-wise activation variance, which limit their reliability. Sparse Autoencoders (SAEs) offer a more nuanced analysis by decomposing the activations of LLMs into sparse linear combination of SAE features. We introduce a novel metric to assess the monolinguality of features obtained from SAEs, discovering that some features are strongly related to specific languages. Additionally, we show that ablating these SAE features only significantly reduces abilities in one language of LLMs, leaving others almost unaffected. Interestingly, we find some languages have multiple synergistic SAE features, and ablating them together yields greater improvement than ablating individually. Moreover, we leverage these SAE-derived language-specific features to enhance steering vectors, achieving control over the language generated by LLMs.
Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction
Authors: Xiaowei Zhu, Yubing Ren, Yanan Cao, Xixun Lin, Fang Fang, Yangxi Li
Date: 2025-05-08 00:00:00
Tasks: Conformal Prediction, Text Detection

The rapid advancement of large language models has raised significant concerns regarding their potential misuse by malicious actors. As a result, developing effective detectors to mitigate these risks has become a critical priority. However, most existing detection methods focus excessively on detection accuracy, often neglecting the societal risks posed by high false positive rates (FPRs). This paper addresses this issue by leveraging Conformal Prediction (CP), which effectively constrains the upper bound of FPRs. While directly applying CP constrains FPRs, it also leads to a significant reduction in detection performance. To overcome this trade-off, this paper proposes a Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction (MCP), which both enforces the FPR constraint and improves detection performance. This paper also introduces RealDet, a high-quality dataset that spans a wide range of domains, ensuring realistic calibration and enabling superior detection performance when combined with MCP. Empirical evaluations demonstrate that MCP effectively constrains FPRs, significantly enhances detection performance, and increases robustness against adversarial attacks across multiple detectors and datasets.
Performance Evaluation of Large Language Models in Bangla Consumer Health Query Summarization
Authors: Ajwad Abrar, Farzana Tabassum, Sabbir Ahmed
Date: 2025-05-08 00:00:00

Consumer Health Queries (CHQs) in Bengali (Bangla), a low-resource language, often contain extraneous details, complicating efficient medical responses. This study investigates the zero-shot performance of nine advanced large language models (LLMs): GPT-3.5-Turbo, GPT-4, Claude-3.5-Sonnet, Llama3-70b-Instruct, Mixtral-8x22b-Instruct, Gemini-1.5-Pro, Qwen2-72b-Instruct, Gemma-2-27b, and Athene-70B, in summarizing Bangla CHQs. Using the BanglaCHQ-Summ dataset comprising 2,350 annotated query-summary pairs, we benchmarked these LLMs using ROUGE metrics against Bangla T5, a fine-tuned state-of-the-art model. Mixtral-8x22b-Instruct emerged as the top performing model in ROUGE-1 and ROUGE-L, while Bangla T5 excelled in ROUGE-2. The results demonstrate that zero-shot LLMs can rival fine-tuned models, achieving high-quality summaries even without task-specific training. This work underscores the potential of LLMs in addressing challenges in low-resource languages, providing scalable solutions for healthcare query summarization.
Scalable Multi-Stage Influence Function for Large Language Models via Eigenvalue-Corrected Kronecker-Factored Parameterization
Authors: Yuntai Bao, Xuhong Zhang, Tianyu Du, Xinkui Zhao, Jiang Zong, Hao Peng, Jianwei Yin
Date: 2025-05-08 00:00:00
Tasks: Attribute

Pre-trained large language models (LLMs) are commonly fine-tuned to adapt to downstream tasks. Since the majority of knowledge is acquired during pre-training, attributing the predictions of fine-tuned LLMs to their pre-training data may provide valuable insights. Influence functions have been proposed as a means to explain model predictions based on training data. However, existing approaches fail to compute ``multi-stage'' influence and lack scalability to billion-scale LLMs. In this paper, we propose the multi-stage influence function to attribute the downstream predictions of fine-tuned LLMs to pre-training data under the full-parameter fine-tuning paradigm. To enhance the efficiency and practicality of our multi-stage influence function, we leverage Eigenvalue-corrected Kronecker-Factored (EK-FAC) parameterization for efficient approximation. Empirical results validate the superior scalability of EK-FAC approximation and the effectiveness of our multi-stage influence function. Additionally, case studies on a real-world LLM, dolly-v2-3b, demonstrate its interpretive power, with exemplars illustrating insights provided by multi-stage influence estimates. Our code is public at https://github.com/colored-dye/multi_stage_influence_function.