pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Response Generation Method Selection for Fine-Tuning Large Language Models
Authors: Xuan Ren, Qi Chen, Lingqiao Liu
Date: 2025-02-17 00:00:00
Tasks: Response Generation

The training data for fine-tuning large language models (LLMs) is typically structured as input-output pairs. However, for many tasks, there can be multiple equally valid output variations for the same input. Recent studies have observed that the choice of output variation used in training can affect the model's performance. This raises an important question: how can we generate the most effective output from the many possible response generation strategy options? Rather than relying on the traditional but resource-intensive train-and-evaluate approach, this paper proposes a scalable, approximate method for estimating the quality of a small subset of generated training data derived from the same input. We then evaluate how well this small subset of generated output fits the target model we are trying to train. We present a large-scale benchmark covering diverse reasoning-based datasets to support our study. The central idea is that a good output should closely resemble the output generated by the target LLM. We formalize this 'closeness' as the expected alignment score between a candidate output and the output sampled from the target LLM. We connect this measurement to the perplexity metric used in previous literature and demonstrate that leveraging an alignment-based metric can provide better predictions of model performance. Using this strategy, we can evaluate a small subset of the generated output from each response generation strategy option, then select the most effective strategy. We show that an LLM trained on data generated by the selected strategy could lead to a significant performance gain in many cases.
Personality Editing for Language Models through Relevant Knowledge Editing
Authors: Seojin Hwang, Yumin Kim, Byeongjeong Kim, Hwanhee Lee
Date: 2025-02-17 00:00:00
Tasks: knowledge editing

Large Language Models (LLMs) play a vital role in applications like conversational agents and content creation, where controlling a model's personality is crucial for maintaining tone, consistency, and engagement. However, traditional prompt-based techniques for controlling personality often fall short, as they do not effectively mitigate the model's inherent biases. In this paper, we introduce a novel method PALETTE that enhances personality control through knowledge editing. By generating adjustment queries inspired by psychological assessments, our approach systematically adjusts responses to personality-related queries similar to modifying factual knowledge, thereby achieving controlled shifts in personality traits. Experimental results from both automatic and human evaluations demonstrate that our method enables more stable and well-balanced personality control in LLMs.
Exploring Translation Mechanism of Large Language Models
Authors: Hongbin Zhang, Kehai Chen, Xuefeng Bai, Xiucheng Li, Min Zhang
Date: 2025-02-17 00:00:00
Tasks: Translation

Large language models (LLMs) have succeeded remarkably in multilingual translation tasks. However, the inherent translation mechanisms of LLMs remain poorly understood, largely due to sophisticated architectures and vast parameter scales. In response to this issue, this study explores the translation mechanism of LLM from the perspective of computational components (e.g., attention heads and MLPs). Path patching is utilized to explore causal relationships between components, detecting those crucial for translation tasks and subsequently analyzing their behavioral patterns in human-interpretable terms. Comprehensive analysis reveals that translation is predominantly facilitated by a sparse subset of specialized attention heads (less than 5\%), which extract source language, indicator, and positional features. MLPs subsequently integrate and process these features by transiting towards English-centric latent representations. Notably, building on the above findings, targeted fine-tuning of only 64 heads achieves translation improvement comparable to full-parameter tuning while preserving general capabilities.
FineFilter: A Fine-grained Noise Filtering Mechanism for Retrieval-Augmented Large Language Models
Authors: Qianchi Zhang, Hainan Zhang, Liang Pang, Ziwei Wang, Hongwei Zheng, Yongxin Tong, Zhiming Zheng
Date: 2025-02-17 00:00:00
Tasks: RAG, Reranking, Re-Ranking, Retrieval, Retrieval-augmented Generation

Retrieved documents containing noise will hinder Retrieval-Augmented Generation (RAG) from detecting answer clues, necessitating noise filtering mechanisms to enhance accuracy. Existing methods use reranking or summarization to identify the most relevant sentences, but directly and accurately locating answer clues from these large-scale and complex documents remains challenging. Unlike these document-level operations, we treat noise filtering as a sentence-level MinMax optimization problem: first identifying potential clues from multiple documents, then ranking them by relevance, and finally retaining the minimum number of clues through truncation. In this paper, we propose FineFilter, a novel fine-grained noise filtering mechanism for RAG, consisting of a clue extractor, a reranker, and a truncator. We optimize each module to tackle complex reasoning challenges: (1) The clue extractor first uses sentences containing the answer and similar ones as fine-tuning targets, aiming to extract sufficient potential clues; (2) The reranker is trained to prioritize effective clues based on the real feedback from the generation module, with clues capable of generating correct answers as positive samples and others as negative; (3) The truncator takes the minimum number of clues needed to answer the question (truncation point) as fine-tuning targets, and performs truncation on the reranked clues to achieve fine-grained noise filtering. Experiments on three QA datasets demonstrate that FineFilter significantly improves QA performance over baselines on both LLaMA3 and Mistral. Further analysis confirms its effectiveness in complex reasoning, robustness to unreliable retrieval, and generalization to different scenarios.
Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis
Authors: Xu Wang, Yan Hu, Wenyu Du, Reynold Cheng, Benyou Wang, Difan Zou
Date: 2025-02-17 00:00:00

Fine-tuning significantly improves the performance of Large Language Models (LLMs), yet its underlying mechanisms remain poorly understood. This paper aims to provide an in-depth interpretation of the fine-tuning process through circuit analysis, a popular tool in Mechanistic Interpretability (MI). Unlike previous studies \cite{prakash2024finetuningenhancesexistingmechanisms,chhabra2024neuroplasticity} that focus on tasks where pre-trained models already perform well, we develop a set of mathematical tasks where fine-tuning yields substantial performance gains, which are closer to the practical setting. In our experiments, we identify circuits at various checkpoints during fine-tuning and examine the interplay between circuit analysis, fine-tuning methods, and task complexities. First, we find that while circuits maintain high node similarity before and after fine-tuning, their edges undergo significant changes, which is in contrast to the previous work \cite{prakash2024finetuningenhancesexistingmechanisms,chhabra2024neuroplasticity} that show circuits only add some additional components after fine-tuning. Based on these observations, we develop a circuit-aware Low-Rank Adaptation (LoRA) method, which assigns ranks to layers based on edge changes in the circuits. Experimental results demonstrate that our circuit-based LoRA algorithm achieves an average performance improvement of 2.46\% over standard LoRA with similar parameter sizes. Furthermore, we explore how combining circuits from subtasks can enhance fine-tuning in compositional tasks, providing new insights into the design of such tasks and deepening the understanding of circuit dynamics and fine-tuning mechanisms.
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis
Authors: Chengyan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Barbara Plank, Yun Xue
Date: 2025-02-17 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Language Model Evaluation, Language Modeling, Language Modelling, Large Language Model, Sentiment Analysis, Transfer Learning, Triplet

Aspect-based sentiment analysis (ABSA) is a crucial task in information extraction and sentiment analysis, aiming to identify aspects with associated sentiment elements in text. However, existing ABSA datasets are predominantly English-centric, limiting the scope for multilingual evaluation and research. To bridge this gap, we present M-ABSA, a comprehensive dataset spanning 7 domains and 21 languages, making it the most extensive multilingual parallel dataset for ABSA to date. Our primary focus is on triplet extraction, which involves identifying aspect terms, aspect categories, and sentiment polarities. The dataset is constructed through an automatic translation process with human review to ensure quality. We perform extensive experiments using various baselines to assess performance and compatibility on M-ABSA. Our empirical findings highlight that the dataset enables diverse evaluation tasks, such as multilingual and multi-domain transfer learning, and large language model evaluation, underscoring its inclusivity and its potential to drive advancements in multilingual ABSA research.
Text Classification in the LLM Era - Where do we stand?
Authors: Sowmya Vajjala, Shwetali Shimangaud
Date: 2025-02-17 00:00:00
Tasks: Classification, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification, zero-shot-classification, Zero-Shot Learning

Large Language Models revolutionized NLP and showed dramatic performance improvements across several tasks. In this paper, we investigated the role of such language models in text classification and how they compare with other approaches relying on smaller pre-trained language models. Considering 32 datasets spanning 8 languages, we compared zero-shot classification, few-shot fine-tuning and synthetic data based classifiers with classifiers built using the complete human labeled dataset. Our results show that zero-shot approaches do well for sentiment classification, but are outperformed by other approaches for the rest of the tasks, and synthetic data sourced from multiple LLMs can build better classifiers than zero-shot open LLMs. We also see wide performance disparities across languages in all the classification scenarios. We expect that these findings would guide practitioners working on developing text classification systems across languages.
LLMs as a synthesis between symbolic and continuous approaches to language
Authors: Gemma Boleda
Date: 2025-02-17 00:00:00
Tasks: Deep Learning

Since the middle of the 20th century, a fierce battle is being fought between symbolic and continuous approaches to language and cognition. The success of deep learning models, and LLMs in particular, has been alternatively taken as showing that the continuous camp has won, or dismissed as an irrelevant engineering development. However, in this position paper I argue that deep learning models for language actually represent a synthesis between the two traditions. This is because 1) deep learning architectures allow for both continuous/distributed and symbolic/discrete-like representations and computations; 2) models trained on language make use this flexibility. In particular, I review recent research in mechanistic interpretability that showcases how a substantial part of morphosyntactic knowledge is encoded in a near-discrete fashion in LLMs. This line of research suggests that different behaviors arise in an emergent fashion, and models flexibly alternate between the two modes (and everything in between) as needed. This is possibly one of the main reasons for their wild success; and it is also what makes them particularly interesting for the study of language and cognition. Is it time for peace?
Exploring Large Language Models in Healthcare: Insights into Corpora Sources, Customization Strategies, and Evaluation Metrics
Authors: Shuqi Yang, Mingrui Jing, Shuai Wang, Jiaxin Kou, Manfei Shi, Weijie Xing, Yan Hu, Zheng Zhu
Date: 2025-02-17 00:00:00
Tasks: Articles, Fairness, Prompt Engineering, Retrieval-augmented Generation

This study reviewed the use of Large Language Models (LLMs) in healthcare, focusing on their training corpora, customization techniques, and evaluation metrics. A systematic search of studies from 2021 to 2024 identified 61 articles. Four types of corpora were used: clinical resources, literature, open-source datasets, and web-crawled data. Common construction techniques included pre-training, prompt engineering, and retrieval-augmented generation, with 44 studies combining multiple methods. Evaluation metrics were categorized into process, usability, and outcome metrics, with outcome metrics divided into model-based and expert-assessed outcomes. The study identified critical gaps in corpus fairness, which contributed to biases from geographic, cultural, and socio-economic factors. The reliance on unverified or unstructured data highlighted the need for better integration of evidence-based clinical guidelines. Future research should focus on developing a tiered corpus architecture with vetted sources and dynamic weighting, while ensuring model transparency. Additionally, the lack of standardized evaluation frameworks for domain-specific models called for comprehensive validation of LLMs in real-world healthcare settings.
Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu
Authors: Renhao Pei, Yihong Liu, Peiqin Lin, François Yvon, Hinrich Schütze
Date: 2025-02-17 00:00:00
Tasks: Data Augmentation, In-Context Learning, Machine Translation, Translation

In-context machine translation (MT) with large language models (LLMs) is a promising approach for low-resource MT, as it can readily take advantage of linguistic resources such as grammar books and dictionaries. Such resources are usually selectively integrated into the prompt so that LLMs can directly perform translation without any specific training, via their in-context learning capability (ICL). However, the relative importance of each type of resource e.g., dictionary, grammar book, and retrieved parallel examples, is not entirely clear. To address this gap, this study systematically investigates how each resource and its quality affects the translation performance, with the Manchu language as our case study. To remove any prior knowledge of Manchu encoded in the LLM parameters and single out the effect of ICL, we also experiment with an encrypted version of Manchu texts. Our results indicate that high-quality dictionaries and good parallel examples are very helpful, while grammars hardly help. In a follow-up study, we showcase a promising application of in-context MT: parallel data augmentation as a way to bootstrap the conventional MT model. When monolingual data abound, generating synthetic parallel data through in-context MT offers a pathway to mitigate data scarcity and build effective and efficient low-resource neural MT systems.
VAQUUM: Are Vague Quantifiers Grounded in Visual Data?
Authors: Hugh Mee Wong, Rick Nouwen, Albert Gatt
Date: 2025-02-17 00:00:00

Vague quantifiers such as "a few" and "many" are influenced by many contextual factors, including how many objects are present in a given context. In this work, we evaluate the extent to which vision-and-language models (VLMs) are compatible with humans when producing or judging the appropriateness of vague quantifiers in visual contexts. We release a novel dataset, VAQUUM, containing 20300 human ratings on quantified statements across a total of 1089 images. Using this dataset, we compare human judgments and VLM predictions using three different evaluation methods. Our findings show that VLMs, like humans, are influenced by object counts in vague quantifier use. However, we find significant inconsistencies across models in different evaluation settings, suggesting that judging and producing vague quantifiers rely on two different processes.
Revisiting Classification Taxonomy for Grammatical Errors
Authors: Deqing Zou, Jingheng Ye, Yulu Liu, Yu Wu, Zishan Xu, Yinghui Li, Hai-Tao Zheng, Bingxu An, Zhao Wei, Yong Xu
Date: 2025-02-17 00:00:00
Tasks: Classification

Grammatical error classification plays a crucial role in language learning systems, but existing classification taxonomies often lack rigorous validation, leading to inconsistencies and unreliable feedback. In this paper, we revisit previous classification taxonomies for grammatical errors by introducing a systematic and qualitative evaluation framework. Our approach examines four aspects of a taxonomy, i.e., exclusivity, coverage, balance, and usability. Then, we construct a high-quality grammatical error classification dataset annotated with multiple classification taxonomies and evaluate them grounding on our proposed evaluation framework. Our experiments reveal the drawbacks of existing taxonomies. Our contributions aim to improve the precision and effectiveness of error analysis, providing more understandable and actionable feedback for language learners.
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation
Authors: Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, Yutong Xie, Imran Razzak, ZongYuan Ge, Jionglong Su, Junjun He, Yu Qiao
Date: 2025-02-17 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model

Recent multimodal large language models (MLLMs) have demonstrated significant potential in open-ended conversation, generating more accurate and personalized responses. However, their abilities to memorize, recall, and reason in sustained interactions within real-world scenarios remain underexplored. This paper introduces MMRC, a Multi-Modal Real-world Conversation benchmark for evaluating six core open-ended abilities of MLLMs: information extraction, multi-turn reasoning, information update, image management, memory recall, and answer refusal. With data collected from real-world scenarios, MMRC comprises 5,120 conversations and 28,720 corresponding manually labeled questions, posing a significant challenge to existing MLLMs. Evaluations on 20 MLLMs in MMRC indicate an accuracy drop during open-ended interactions. We identify four common failure patterns: long-term memory degradation, inadequacies in updating factual knowledge, accumulated assumption of error propagation, and reluctance to say no. To mitigate these issues, we propose a simple yet effective NOTE-TAKING strategy, which can record key information from the conversation and remind the model during its responses, enhancing conversational capabilities. Experiments across six MLLMs demonstrate significant performance improvements.
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models
Authors: Jiamin Su, Yibo Yan, Fangteng Fu, Han Zhang, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu
Date: 2025-02-17 00:00:00
Tasks: Automated Essay Scoring, Feature Engineering, Sentence

Automated Essay Scoring (AES) plays a crucial role in educational assessment by providing scalable and consistent evaluations of writing tasks. However, traditional AES systems face three major challenges: (1) reliance on handcrafted features that limit generalizability, (2) difficulty in capturing fine-grained traits like coherence and argumentation, and (3) inability to handle multimodal contexts. In the era of Multimodal Large Language Models (MLLMs), we propose EssayJudge, the first multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits. By leveraging MLLMs' strengths in trait-specific scoring and multimodal context understanding, EssayJudge aims to offer precise, context-rich evaluations without manual feature engineering, addressing longstanding AES limitations. Our experiments with 18 representative MLLMs reveal gaps in AES performance compared to human evaluation, particularly in discourse-level traits, highlighting the need for further advancements in MLLM-based AES research.
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages
Authors: Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas, Meriem Beloucif, Christine de Kock, Nirmal Surange, Daniela Teodorescu, Ibrahim Said Ahmad, David Ifeoluwa Adelani, Alham Fikri Aji, Felermino D. M. A. Ali, Ilseyar Alimova, Vladimir Araujo, Nikolay Babakov, Naomi Baes, Ana-Maria Bucur, Andiswa Bukula, Guanqun Cao, Rodrigo Tufino Cardenas, Rendi Chevi, Chiamaka Ijeoma Chukwuneke, Alexandra Ciobotaru, Daryna Dementieva, Murja Sani Gadanya, Robert Geislinger, Bela Gipp, Oumaima Hourrane, Oana Ignat, Falalu Ibrahim Lawan, Rooweither Mabuya, Rahmad Mahendra, Vukosi Marivate, Andrew Piper, Alexander Panchenko, Charles Henrique Porto Ferreira, Vitaly Protasov, Samuel Rutunda, Manish Shrivastava, Aura Cristina Udrea, Lilian Diana Awuor Wanzare, Sophie Wu, Florian Valentin Wunderlich, Hanif Muhammad Zhafran, Tianhui Zhang, Yi Zhou, Saif M. Mohammad
Date: 2025-02-17 00:00:00
Tasks: Emotion Recognition

People worldwide use language in subtle and complex ways to express emotions. While emotion recognition -- an umbrella term for several NLP tasks -- significantly impacts different applications in NLP and other fields, most work in the area is focused on high-resource languages. Therefore, this has led to major disparities in research and proposed solutions, especially for low-resource languages that suffer from the lack of high-quality datasets. In this paper, we present BRIGHTER-- a collection of multilabeled emotion-annotated datasets in 28 different languages. BRIGHTER covers predominantly low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances from various domains annotated by fluent speakers. We describe the data collection and annotation processes and the challenges of building these datasets. Then, we report different experimental results for monolingual and crosslingual multi-label emotion identification, as well as intensity-level emotion recognition. We investigate results with and without using LLMs and analyse the large variability in performance across languages and text domains. We show that BRIGHTER datasets are a step towards bridging the gap in text-based emotion recognition and discuss their impact and utility.
On Representational Dissociation of Language and Arithmetic in Large Language Models
Authors: Riku Kisako, Tatsuki Kuribayashi, Ryohei Sasano
Date: 2025-02-17 00:00:00
Tasks: Arithmetic Reasoning

The association between language and (non-linguistic) thinking ability in humans has long been debated, and recently, neuroscientific evidence of brain activity patterns has been considered. Such a scientific context naturally raises an interdisciplinary question -- what about such a language-thought dissociation in large language models (LLMs)? In this paper, as an initial foray, we explore this question by focusing on simple arithmetic skills (e.g., $1+2=$ ?) as a thinking ability and analyzing the geometry of their encoding in LLMs' representation space. Our experiments with linear classifiers and cluster separability tests demonstrate that simple arithmetic equations and general language input are encoded in completely separated regions in LLMs' internal representation space across all the layers, which is also supported with more controlled stimuli (e.g., spelled-out equations). These tentatively suggest that arithmetic reasoning is mapped into a distinct region from general language input, which is in line with the neuroscientific observations of human brain activations, while we also point out their somewhat cognitively implausible geometric properties.