pwc-archive/papers-with-abstracts huggingface dataset:
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts
Authors: Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang
Date: 2025-03-09 00:00:00

Process-driven dialogue systems, which operate under strict predefined process constraints, are essential in customer service and equipment maintenance scenarios. Although Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, they still struggle to solve these strictly constrained dialogue tasks. To address this challenge, we construct Process Flow Dialogue (PFDial) dataset, which contains 12,705 high-quality Chinese dialogue instructions derived from 440 flowcharts containing 5,055 process nodes. Based on PlantUML specification, each UML flowchart is converted into atomic dialogue units i.e., structured five-tuples. Experimental results demonstrate that a 7B model trained with merely 800 samples, and a 0.5B model trained on total data both can surpass 90% accuracy. Additionally, the 8B model can surpass GPT-4o up to 43.88% with an average of 11.00%. We further evaluate models' performance on challenging backward transitions in process flows and conduct an in-depth analysis of various dataset formats to reveal their impact on model performance in handling decision and sequential branches. The data is released in https://github.com/KongLongGeFDU/PFDial.
Alignment for Efficient Tool Calling of Large Language Models
Authors: Hongshen Xu, Zihan Wang, Zichen Zhu, Lei Pan, Xingyu Chen, Lu Chen, Kai Yu
Date: 2025-03-09 00:00:00
Tasks: Decision Making

Recent advancements in tool learning have enabled large language models (LLMs) to integrate external tools, enhancing their task performance by expanding their knowledge boundaries. However, relying on tools often introduces tradeoffs between performance, speed, and cost, with LLMs sometimes exhibiting overreliance and overconfidence in tool usage. This paper addresses the challenge of aligning LLMs with their knowledge boundaries to make more intelligent decisions about tool invocation. We propose a multi objective alignment framework that combines probabilistic knowledge boundary estimation with dynamic decision making, allowing LLMs to better assess when to invoke tools based on their confidence. Our framework includes two methods for knowledge boundary estimation, consistency based and absolute estimation, and two training strategies for integrating these estimates into the model decision making process. Experimental results on various tool invocation scenarios demonstrate the effectiveness of our framework, showing significant improvements in tool efficiency by reducing unnecessary tool usage.
Delusions of Large Language Models
Authors: Hongshen Xu, Zixv yang, Zichen Zhu, Kunyao Lan, Zihan Wang, Mengyue Wu, Ziwei Ji, Lu Chen, Pascale Fung, Kai Yu
Date: 2025-03-09 00:00:00
Tasks: Question Answering, Retrieval-augmented Generation

Large Language Models often generate factually incorrect but plausible outputs, known as hallucinations. We identify a more insidious phenomenon, LLM delusion, defined as high belief hallucinations, incorrect outputs with abnormally high confidence, making them harder to detect and mitigate. Unlike ordinary hallucinations, delusions persist with low uncertainty, posing significant challenges to model reliability. Through empirical analysis across different model families and sizes on several Question Answering tasks, we show that delusions are prevalent and distinct from hallucinations. LLMs exhibit lower honesty with delusions, which are harder to override via finetuning or self reflection. We link delusion formation with training dynamics and dataset noise and explore mitigation strategies such as retrieval augmented generation and multi agent debating to mitigate delusions. By systematically investigating the nature, prevalence, and mitigation of LLM delusions, our study provides insights into the underlying causes of this phenomenon and outlines future directions for improving model reliability.
Topology of Syntax Networks across Languages
Authors: Juan Soria-Postigo, Luis F Seoane
Date: 2025-03-09 00:00:00
Tasks: Sentence

Syntax connects words to each other in very specific ways. Two words are syntactically connected if they depend directly on each other. Syntactic connections usually happen within a sentence. Gathering all those connection across several sentences gives birth to syntax networks. Earlier studies in the field have analysed the structure and properties of syntax networks trying to find clusters/phylogenies of languages that share similar network features. The results obtained in those studies will be put to test in this thesis by increasing both the number of languages and the number of properties considered in the analysis. Besides that, language networks of particular languages will be inspected in depth by means of a novel network analysis [25]. Words (nodes of the network) will be clustered into topological communities whose members share similar features. The properties of each of these communities will be thoroughly studied along with the Part of Speech (grammatical class) of each word. Results across different languages will also be compared in an attempt to discover universally preserved structural patterns across syntax networks.
Gender Encoding Patterns in Pretrained Language Model Representations
Authors: Mahdi Zakizadeh, Mohammad Taher Pilehvar
Date: 2025-03-09 00:00:00
Tasks: Language Modeling, Language Modelling, model

Gender bias in pretrained language models (PLMs) poses significant social and ethical challenges. Despite growing awareness, there is a lack of comprehensive investigation into how different models internally represent and propagate such biases. This study adopts an information-theoretic approach to analyze how gender biases are encoded within various encoder-based architectures. We focus on three key aspects: identifying how models encode gender information and biases, examining the impact of bias mitigation techniques and fine-tuning on the encoded biases and their effectiveness, and exploring how model design differences influence the encoding of biases. Through rigorous and systematic investigation, our findings reveal a consistent pattern of gender encoding across diverse models. Surprisingly, debiasing techniques often exhibit limited efficacy, sometimes inadvertently increasing the encoded bias in internal representations while reducing bias in model output distributions. This highlights a disconnect between mitigating bias in output distributions and addressing its internal representations. This work provides valuable guidance for advancing bias mitigation strategies and fostering the development of more equitable language models.
Effectiveness of Zero-shot-CoT in Japanese Prompts
Authors: Shusuke Takayama, Ian Frank
Date: 2025-03-09 00:00:00
Tasks: Abstract Algebra, College Mathematics, MMLU, Multi-task Language Understanding

We compare the effectiveness of zero-shot Chain-of-Thought (CoT) prompting in Japanese and English using ChatGPT-3.5 and 4o-mini. The technique of zero-shot CoT, which involves appending a phrase such as "Let's think step by step" to a prompt to encourage reasoning before answering, has been shown to offer LLM performance improvements in mathematical and reasoning tasks, particularly in English. We investigate how these effects transfer to Japanese using the Japanese Multi-task Language Understanding Benchmark (JMMLU) and the Multi-task Language Understanding Benchmark (MMLU). Our results show that while zero-shot CoT prompting can lead to notable performance gains for some prompt categories in GPT-3.5, its impact in GPT-4o-mini is associated with significant performance declines. However, for Japanese prompts there remain certain categories, such as college mathematics and abstract algebra, that still exhibit improvements, despite the broader trend of diminishing effectiveness in more advanced models.
Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators
Authors: Feng Gu, Zongxia Li, Carlos Rafael Colon, Benjamin Evans, Ishani Mondal, Jordan Lee Boyd-Graber
Date: 2025-03-09 00:00:00

Event annotation is important for identifying market changes, monitoring breaking news, and understanding sociological trends. Although expert annotators set the gold standards, human coding is expensive and inefficient. Unlike information extraction experiments that focus on single contexts, we evaluate a holistic workflow that removes irrelevant documents, merges documents about the same event, and annotates the events. Although LLM-based automated annotations are better than traditional TF-IDF-based methods or Event Set Curation, they are still not reliable annotators compared to human experts. However, adding LLMs to assist experts for Event Set Curation can reduce the time and mental effort required for Variable Annotation. When using LLMs to extract event variables to assist expert annotators, they agree more with the extracted variables than fully automated LLMs for annotation.
Dr Genre: Reinforcement Learning from Decoupled LLM Feedback for Generic Text Rewriting
Authors: Yufei Li, John Nham, Ganesh Jawahar, Lei Shu, David Uthus, Yun-Hsuan Sung, Chengrun Yang, Itai Rolnick, Yi Qiao, Cong Liu
Date: 2025-03-09 00:00:00
Tasks: Instruction Following, Large Language Model, Style Transfer

Generic text rewriting is a prevalent large language model (LLM) application that covers diverse real-world tasks, such as style transfer, fact correction, and email editing. These tasks vary in rewriting objectives (e.g., factual consistency vs. semantic preservation), making it challenging to develop a unified model that excels across all dimensions. Existing methods often specialize in either a single task or a specific objective, limiting their generalizability. In this work, we introduce a generic model proficient in factuality, stylistic, and conversational rewriting tasks. To simulate real-world user rewrite requests, we construct a conversational rewrite dataset, ChatRewrite, that presents ``natural''-sounding instructions, from raw emails using LLMs. Combined with other popular rewrite datasets, including LongFact for the factuality rewrite task and RewriteLM for the stylistic rewrite task, this forms a broad benchmark for training and evaluating generic rewrite models. To align with task-specific objectives, we propose Dr Genre, a Decoupled-reward learning framework for Generic rewriting, that utilizes objective-oriented reward models with a task-specific weighting. Evaluation shows that \approach delivers higher-quality rewrites across all targeted tasks, improving objectives including instruction following (agreement), internal consistency (coherence), and minimal unnecessary edits (conciseness).
On the Mutual Influence of Gender and Occupation in LLM Representations
Authors: Haozhe An, Connor Baumler, Abhilasha Sancheti, Rachel Rudinger
Date: 2025-03-09 00:00:00
Tasks: Bias Detection, Occupation prediction

We examine LLM representations of gender for first names in various occupational contexts to study how occupations and the gender perception of first names in LLMs influence each other mutually. We find that LLMs' first-name gender representations correlate with real-world gender statistics associated with the name, and are influenced by the co-occurrence of stereotypically feminine or masculine occupations. Additionally, we study the influence of first-name gender representations on LLMs in a downstream occupation prediction task and their potential as an internal metric to identify extrinsic model biases. While feminine first-name embeddings often raise the probabilities for female-dominated jobs (and vice versa for male-dominated jobs), reliably using these internal gender representations for bias detection remains challenging.
Enhanced Multi-Tuple Extraction for Alloys: Integrating Pointer Networks and Augmented Attention
Authors: Mengzhe Hei, Zhouran Zhang, Qingbao Liu, Yan Pan, Xiang Zhao, Yongqian Peng, Yicong Ye, Xin Zhang, Shuxin Bai
Date: 2025-03-10 00:00:00

Extracting high-quality structured information from scientific literature is crucial for advancing material design through data-driven methods. Despite the considerable research in natural language processing for dataset extraction, effective approaches for multi-tuple extraction in scientific literature remain scarce due to the complex interrelations of tuples and contextual ambiguities. In the study, we illustrate the multi-tuple extraction of mechanical properties from multi-principal-element alloys and presents a novel framework that combines an entity extraction model based on MatSciBERT with pointer networks and an allocation model utilizing inter- and intra-entity attention. Our rigorous experiments on tuple extraction demonstrate impressive F1 scores of 0.963, 0.947, 0.848, and 0.753 across datasets with 1, 2, 3, and 4 tuples, confirming the effectiveness of the model. Furthermore, an F1 score of 0.854 was achieved on a randomly curated dataset. These results highlight the model's capacity to deliver precise and structured information, offering a robust alternative to large language models and equipping researchers with essential data for fostering data-driven innovations.
Lost-in-the-Middle in Long-Text Generation: Synthetic Dataset, Evaluation Framework, and Mitigation
Authors: Junhao Zhang, Richong Zhang, Fanshuang Kong, Ziyang Miao, Yanhan Ye, Yaowei Zheng
Date: 2025-03-10 00:00:00
Tasks: Text Generation

Existing long-text generation methods primarily concentrate on producing lengthy texts from short inputs, neglecting the long-input and long-output tasks. Such tasks have numerous practical applications while lacking available benchmarks. Moreover, as the input grows in length, existing methods inevitably encounter the "lost-in-the-middle" phenomenon. In this paper, we first introduce a Long Input and Output Benchmark (LongInOutBench), including a synthetic dataset and a comprehensive evaluation framework, addressing the challenge of the missing benchmark. We then develop the Retrieval-Augmented Long-Text Writer (RAL-Writer), which retrieves and restates important yet overlooked content, mitigating the "lost-in-the-middle" issue by constructing explicit prompts. We finally employ the proposed LongInOutBench to evaluate our RAL-Writer against comparable baselines, and the results demonstrate the effectiveness of our approach. Our code has been released at https://github.com/OnlyAR/RAL-Writer.
A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization
Authors: Dan Sun, Jacky He, Hanlu Zhang, Zhen Qi, Hongye Zheng, Xiaokai Wang
Date: 2025-03-10 00:00:00
Tasks: Text Summarization

This paper proposes a medical text summarization method based on LongFormer, aimed at addressing the challenges faced by existing models when processing long medical texts. Traditional summarization methods are often limited by short-term memory, leading to information loss or reduced summary quality in long texts. LongFormer, by introducing long-range self-attention, effectively captures long-range dependencies in the text, retaining more key information and improving the accuracy and information retention of summaries. Experimental results show that the LongFormer-based model outperforms traditional models, such as RNN, T5, and BERT in automatic evaluation metrics like ROUGE. It also receives high scores in expert evaluations, particularly excelling in information retention and grammatical accuracy. However, there is still room for improvement in terms of conciseness and readability. Some experts noted that the generated summaries contain redundant information, which affects conciseness. Future research will focus on further optimizing the model structure to enhance conciseness and fluency, achieving more efficient medical text summarization. As medical data continues to grow, automated summarization technology will play an increasingly important role in fields such as medical research, clinical decision support, and knowledge management.
KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus
Authors: Xiaoming Shi, Zeming Liu, Yiming Lei, Chenkai Zhang, Haitao Leng, Chuan Wang, Qingjie Liu, Wanxiang Che, Shaoguo Liu, Size Li, Yunhong Wang
Date: 2025-03-10 00:00:00
Tasks: In-Context Learning, Question Answering

Video-based dialogue systems, such as education assistants, have compelling application value, thereby garnering growing interest. However, the current video-based dialogue systems are limited by their reliance on a single dialogue type, which hinders their versatility in practical applications across a range of scenarios, including question-answering, emotional dialog, etc. In this paper, we identify this challenge as how to generate video-driven multilingual mixed-type dialogues. To mitigate this challenge, we propose a novel task and create a human-to-human video-driven multilingual mixed-type dialogue corpus, termed KwaiChat, containing a total of 93,209 videos and 246,080 dialogues, across 4 dialogue types, 30 domains, 4 languages, and 13 topics. Additionally, we establish baseline models on KwaiChat. An extensive analysis of 7 distinct LLMs on KwaiChat reveals that GPT-4o achieves the best performance but still cannot perform well in this situation even with the help of in-context learning and fine-tuning, which indicates that the task is not trivial and needs further research.
Lshan-1.0 Technical Report
Authors: Haotian Chen, Yanyu Xu, Boyan Wang, Chaoyue Zhao, Xiaoyu Han, Fang Wang, Lizhen Cui, Yonghui Xu
Date: 2025-03-10 00:00:00
Tasks: Large Language Model

In this report, we introduce our first-generation reasoning model, Lshan-1.0, a large language model designed for the highly specialized Chinese legal domain, offering comprehensive capabilities to meet diverse realistic needs. Existing legal LLMs face two primary challenges. Firstly, their design and evaluation are predominantly driven by computer science perspectives, leading to insufficient incorporation of legal expertise and logic, which is crucial for high-precision legal applications, such as handling complex prosecutorial tasks. Secondly, these models often underperform due to a lack of comprehensive training data from the legal domain, limiting their ability to effectively address real-world legal scenarios. To address this, we first compile millions of legal documents covering over 20 types of crimes from 31 provinces in China for model training. From the extensive dataset, we further select high-quality for supervised fine-tuning, ensuring enhanced relevance and precision. The model further undergoes large-scale reinforcement learning without additional supervision, emphasizing the enhancement of its reasoning capabilities and explainability. To validate its effectiveness in complex legal applications, we also conduct human evaluations with legal experts. We develop fine-tuned models based on DeepSeek-R1-Distilled versions, available in three dense configurations: 14B, 32B, and 70B.
CtrlRAG: Black-box Adversarial Attacks Based on Masked Language Models in Retrieval-Augmented Language Generation
Authors: Runqi Sui
Date: 2025-03-10 00:00:00
Tasks: Hallucination, Language Modeling, Language Modelling, RAG, Retrieval, Retrieval-augmented Generation, Text Generation

Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by integrating external knowledge bases. However, this integration introduces a new security threat: adversaries can exploit the retrieval mechanism to inject malicious content into the knowledge base, thereby influencing the generated responses. Based on this attack vector, we propose CtrlRAG, a novel attack method designed for RAG system in the black-box setting, which aligns with real-world scenarios. Unlike existing attack methods, CtrlRAG introduces a perturbation mechanism using Masked Language Model (MLM) to dynamically optimize malicious content in response to changes in the retrieved context. Experimental results demonstrate that CtrlRAG outperforms three baseline methods in both Emotional Manipulation and Hallucination Amplification objectives. Furthermore, we evaluate three existing defense mechanisms, revealing their limited effectiveness against CtrlRAG and underscoring the urgent need for more robust defenses.
Exploring Multimodal Perception in Large Language Models Through Perceptual Strength Ratings
Authors: Jonghyun Lee, Dojun Park, Jiwoo Lee, Hoekeon Choi, Sung-Eun Lee
Date: 2025-03-10 00:00:00

This study investigated the multimodal perception of large language models (LLMs), focusing on their ability to capture human-like perceptual strength ratings across sensory modalities. Utilizing perceptual strength ratings as a benchmark, the research compared GPT-3.5, GPT-4, GPT-4o, and GPT-4o-mini, highlighting the influence of multimodal inputs on grounding and linguistic reasoning. While GPT-4 and GPT-4o demonstrated strong alignment with human evaluations and significant advancements over smaller models, qualitative analyses revealed distinct differences in processing patterns, such as multisensory overrating and reliance on loose semantic associations. Despite integrating multimodal capabilities, GPT-4o did not exhibit superior grounding compared to GPT-4, raising questions about their role in improving human-like grounding. These findings underscore how LLMs' reliance on linguistic patterns can both approximate and diverge from human embodied cognition, revealing limitations in replicating sensory experiences.
Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations
Authors: Jiho Jin, Woosung Kang, Junho Myung, Alice Oh
Date: 2025-03-10 00:00:00
Tasks: Form, Multiple-choice, Story Generation

Measuring social bias in large language models (LLMs) is crucial, but existing bias evaluation methods struggle to assess bias in long-form generation. We propose a Bias Benchmark for Generation (BBG), an adaptation of the Bias Benchmark for QA (BBQ), designed to evaluate social bias in long-form generation by having LLMs generate continuations of story prompts. Building our benchmark in English and Korean, we measure the probability of neutral and biased generations across ten LLMs. We also compare our long-form story generation evaluation results with multiple-choice BBQ evaluation, showing that the two approaches produce inconsistent results.
Large Language Models Often Say One Thing and Do Another
Authors: Ruoxi Xu, Hongyu Lin, Xianpei Han, Jia Zheng, Weixiang Zhou, Le Sun, Yingfei Sun
Date: 2025-03-10 00:00:00
Tasks: Word Alignment

As large language models (LLMs) increasingly become central to various applications and interact with diverse user populations, ensuring their reliable and consistent performance is becoming more important. This paper explores a critical issue in assessing the reliability of LLMs: the consistency between their words and deeds. To quantitatively explore this consistency, we developed a novel evaluation benchmark called the Words and Deeds Consistency Test (WDCT). The benchmark establishes a strict correspondence between word-based and deed-based questions across different domains, including opinion vs. action, non-ethical value vs. action, ethical value vs. action, and theory vs. application. The evaluation results reveal a widespread inconsistency between words and deeds across different LLMs and domains. Subsequently, we conducted experiments with either word alignment or deed alignment to observe their impact on the other aspect. The experimental results indicate that alignment only on words or deeds poorly and unpredictably influences the other aspect. This supports our hypothesis that the underlying knowledge guiding LLMs' word or deed choices is not contained within a unified space.