语言大模型 / LLM

LLM-FS-Agent: A Deliberative Role-based Large Language Model Architecture for Transparent Feature Selection

Mohamed Bal-Ghaoui, Fayssal Sabri

机构 * R&D Department, Audensiel Conseil（Audensiel Conseil 研发部） ； Ecole Centrale de Lyon（Lyon 工程学院） 纠错

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.AI、cs.LG

L3Cube-MahaEmotions: A Marathi Emotion Recognition Dataset with Synthetic Annotations using CoTR prompting and Large Language Models

Nidhi Kowtal, Raviraj Joshi

机构 * Pune Institute of Computer Technology（普那计算机技术研究所） ； Indian Institute of Technology Madras（印度理工学院马德拉斯分校） ； L3Cube Labs（L3Cube实验室） 纠错

专题命中 评测与基准 ：large language model(title,abstract)；language model(title,abstract)；prompting(title,abstract)；分类 cs.CL、cs.LG

Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting

Miaosen Luo, Jiesen Long, Zequn Li, Yunying Yang, Yuncheng Jiang, Sijie Mai

机构 * School of Computer Science, South China Normal University（华南师范大学计算机科学学院） ； School of Information Technology in Education, South China Normal University（华南师范大学教育信息技术学院） 纠错

专题命中 评测与基准 ：large language model(title,abstract)；language model(title,abstract)；prompting(title,abstract)；分类 cs.AI、cs.LG

Expediting data extraction using a large language model (LLM) and scoping review protocol: a methodological study within a complex scoping review

James Stewart-Evans, Emma Wilson, Tessa Langley, Andrew Prayle, Angela Hands, Karen Exley, Jo Leonardi-Bee

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments 44 pages, 4 figures

Knockout LLM Assessment: Using Large Language Models for Evaluations through Iterative Pairwise Comparisons

Isik Baran Sandan, Tu Anh Dinh, Jan Niehues

机构 * Karlsruhe Institute of Technology（卡尔斯鲁厄理工大学） 纠错

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments Accepted to GEM @ ACL 2025

No LLM is Free From Bias: A Comprehensive Study of Bias Evaluation in Large Language Models

Charaka Vinayak Kumar, Ashok Urlana, Gopichand Kanumolu, Bala Mallikarjunarao Garlapati, Pruthwik Mishra

机构 * IIIT Hyderabad（印度海得拉尔印度理工学院） ； TCS Research（塔塔咨询服务研究） ； SVNIT Surat（萨瓦尔特理工学院） 纠错

专题命中 评测与基准 ：large language model(title,abstract)；language model(title,abstract)；LLM(title)；prompting(abstract)

Comments 12 pages, 1 figure

MSD-LLM: Predicting Ship Detention in Port State Control Inspections with Large Language Model

Jiongchao Jin, Xiuju Fu, Xiaowei Gao, Tao Cheng, Ran Yan

机构 * Institute of High Performance Computing（高性能计算研究所） ； Agency for Science, Technology and Research（科技研究局） ； Department of Earth Science and Engineering（地球科学与工程系） ； Imperial College London（伦敦帝国理工学院） ； SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering（空间时间实验室，土木、环境与测绘工程系） ； University College London（伦敦大学学院） ； School of Civil and Environmental Engineering（土木与环境工程学院） ； Nanyang Technological University（南洋理工大学） 纠错

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.AI、cs.LG

MDD-LLM: Towards Accuracy Large Language Models for Major Depressive Disorder Diagnosis

Yuyang Sha, Hongxin Pan, Wei Xu, Weiyu Meng, Gang Luo, Xinyu Du, Xiaobing Zhai, Henry H. Y. Tong, Caijuan Shi, Kefeng Li

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering

Zhou Yu, Xuecheng Ouyang, Zhenwei Shao, Meng Wang, Jun Yu

机构 * Key Laboratory of Complex Systems Modeling and Simulation, the School of Computer Science, Hangzhou Dianzi University（复杂系统建模与仿真重点实验室、计算机科学学院、杭州电子大学） ； HDU-ITMO Joint Institute, Hangzhou Dianzi University（杭州电子大学-ITMO联合学院） ； School of Computer Science and Information Engineering, Hefei University of Technology（计算机科学与信息工程学院、合肥工业大学） ； School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen（智能科学与工程学院、哈尔滨工业大学深圳校区） 纠错

专题命中 评测与基准 ：large language model(title,abstract)；language model(title,abstract)；prompting(title)；LLM(abstract)

Comments An extended journal version of our CVPR 2023 paper, which has been accepted at IEEE T-PAMI 2025. The original conference version can be referred to as the v1 version

LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Minqian Liu, Zhiyang Xu, Xinyi Zhang, Heajun An, Sarvech Qadir, Qi Zhang, Pamela J. Wisniewski, Jin-Hee Cho, Sang Won Lee, Ruoxi Jia, Lifu Huang

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments 20 pages, 7 figures, 4 tables

SKG-LLM: Developing a Mathematical Model for Stroke Knowledge Graph Construction Using Large Language Models

Ali Sarabadani, Kheirolah Rahsepar Fard, Hamid Dalvand

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

AAD-LLM: Adaptive Anomaly Detection Using Large Language Models

Alicia Russell-Gilbert, Alexander Sommers, Andrew Thompson, Logan Cummins, Sudip Mittal, Shahram Rahimi, Maria Seale, Joseph Jaboure, Thomas Arnold, Joshua Church

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.AI、cs.LG

LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model

Tianqianjin Lin, Pengwei Yan, Kaisong Song, Zhuoren Jiang, Yangyang Kang, Jun Lin, Weikang Yuan, Junjie Cao, Changlong Sun, Xiaozhong Liu

专题命中 评测与基准 ：large language model(title,abstract)；language model(title,abstract)；foundation model(title,abstract)；分类 cs.AI、cs.LG

Comments under review

Prompting Large Language Models for Supporting the Differential Diagnosis of Anemia

Elisa Castagnari, Lillian Muyama, Adrien Coulet

专题命中 评测与基准 ：large language model(title,abstract)；language model(title,abstract)；prompting(title,abstract)；分类 cs.CL、cs.AI

Journal ref LLMs4MI 2024 @FLLM 2024, IEEE, Nov 2024, Dubai, United Arab Emirates

Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark

Chanjun Park, Hyeonwoo Kim, Dahyun Kim, Seonghwan Cho, Sanghoon Kim, Sukyung Lee, Yungi Kim, Hwalsuk Lee

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments Accepted at ACL 2024 Main

LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Play

Li-Chun Lu, Shou-Jen Chen, Tsung-Min Pai, Chan-Hung Yu, Hung-yi Lee, Shao-Hua Sun

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments 40 pages, 9 figures, COLM 2024

Prompt4Vis: Prompting Large Language Models with Example Mining and Schema Filtering for Tabular Data Visualization

Shuaimin Li, Xuanang Chen, Yuanfeng Song, Yunze Song, Chen Zhang

专题命中 评测与基准 ：large language model(title,abstract)；language model(title,abstract)；prompting(title)；LLM(abstract)

A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems

Mohamed R. Shoaib, Heba M. Emara, Jun Zhao

专题命中 评测与基准 ：large language model(title,abstract)；language model(title,abstract)；foundation model(title,abstract)；分类 cs.CL、cs.AI

Comments This paper appears in International Conference on Computer and Applications (ICCA) 2023

Investigating the Efficacy of Large Language Models in Reflective Assessment Methods through Chain of Thoughts Prompting

Baphumelele Masikisiki, Vukosi Marivate, Yvette Hlope

专题命中 评测与基准 ：large language model(title,abstract)；language model(title,abstract)；prompting(title,abstract)；分类 cs.CL、cs.AI

Comments Accepted for publication in the Associate Computer Machinery

Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models

Haoyu Gao, Ting-En Lin, Hangyu Li, Min Yang, Yuchuan Wu, Wentao Ma, Yongbin Li

专题命中 评测与基准 ：large language model(title,abstract)；language model(title,abstract)；prompting(title,abstract)；分类 cs.CL、cs.AI

Psy-LLM: Scaling up Global Mental Health Psychological Services with AI-based Large Language Models

Tin Lai, Yukun Shi, Zicong Du, Jiajie Wu, Ken Fu, Yichao Dou, Ziqi Wang

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models

Yen-Ting Lin, Yun-Nung Chen

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments Accepted at 5th NLP4ConvAI

llm-japanese-dataset v0: Construction of Japanese Chat Dataset for Large Language Models and its Methodology

Masanori Hirano, Masahiro Suzuki, Hiroki Sakaji

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

Comments 12 pages

X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages

Feilong Chen, Minglun Han, Haozhi Zhao, Qingyang Zhang, Jing Shi, Shuang Xu, Bo Xu

专题命中 评测与基准 ：LLM(title,abstract)；large language model(title,abstract)；language model(title,abstract)；分类 cs.CL、cs.AI

The Eloquence team submission for task 1 of MLC-SLM challenge

Eloquence团队针对MLC-SLM挑战赛任务1的提交

Lorenzo Concina, Jordi Luque, Alessio Brutti, Marco Matassoni, Yuchen Zhang

机构 * Fondazione Bruno Kessler（布鲁诺·科塞拉基金会） ； Telefónica Innovación Digital, Scientific Group（电信创新数字公司，科学小组） ； University of Essex（埃塞克斯大学） 纠错

专题命中 评测与基准 ：SLM(title,title_cn)；language model(abstract)；foundation model(abstract)；分类 cs.CL

AI总结 该研究针对MLC-SLM挑战赛任务1，探索三种多语言ASR方法，评估基线、利用SLAM-ASR框架训练投影器并研究对比学习与会话上下文的作用。

Comments Technical Report for MLC-SLM Challenge of Interspeech2025

Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data

Prompting-MammAlps：用于相机陷阱数据的细粒度文本到视频检索

Valentin Gabeff, Baptiste Maquignaz, Jennifer Shan, Sepideh Mamooler, Gencer Sumbul, Blair Costelloe, Devis Tuia, Alexander Mathis

机构 * Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院） ； Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所） ； University of Konstanz（康斯坦茨大学） 纠错

专题命中 评测与基准 ：prompting(title,title_cn)；LLM(abstract)；language model(abstract)；分类 cs.AI

AI总结 针对相机陷阱数据自动检索视频的难题，提出Prompting-MammAlps基准及细粒度可解释TVR方法，训练视觉变压器并结合大语言模型处理查询，在基准测试中取得较好成绩，优于零样本VLM。

Comments Accepted at ECCV 2026; Project page: https://cnai.epfl.ch/prompting-mammalps

Reassessing Extractive QA Datasets at Scale: LLM-as-a-Judge and In-Depth Analyses

重新评估大规模抽取式问答数据集：LLM作为评判者与深入分析

Xanh Ho, Jiahao Huang, Florian Boudin, Akiko Aizawa

机构 * National Institute of Informatics, Japan（日本国立信息研究所） ； The University of Tokyo, Japan（东京大学） ； Inria, LS2N, Nantes Université, France（法国Inria、LS2N、南特大学） 纠错

专题命中 评测与基准 ：LLM(title,title_cn)；large language model(abstract)；language model(abstract)；分类 cs.CL

AI总结 本研究系统性地使用LLM作为评判者评估四个抽取式问答数据集，发现其与人类评价的相关性远高于EM和F1，并分析了答案类型敏感性、提示变化和自偏好偏差等因素。

Comments GEM Workshop at ACL 2026; code and data are available at https://github.com/Alab-NII/llm-judge-extract-qa

DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection

DetectRL-X: 向可靠多语言和真实世界LLM生成文本检测迈进

Junchao Wu, Yefeng Liu, Chenyu Zhu, Hao Zhang, Zeyu Wu, Tianqi Shi, Yichao Du, Longyue Wang, Weihua Luo, Jinsong Su, Derek F. Wong

机构 * NLP2 CT Lab, Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学学院NLP2 CT实验室） ； Alibaba Group（阿里巴巴集团） ； Xiamen University（厦门大学） 纠错

专题命中 评测与基准 ：LLM(title,title_cn)；large language model(abstract)；language model(abstract)；分类 cs.CL

AI总结 本文提出DetectRL-X，一个综合性的多语言基准，用于评估先进检测器在8个维度上的性能，通过8种常见商业语言和6种易受LLM滥用的领域人类文本，结合4种流行商业LLM生成文本及润色、扩展和缩写等AI辅助写作操作，分析不同语言、领域、生成器、攻击策略、文本长度和润色操作对检测器性能的影响，以强化多语言和语言特定检测器。

Comments ACL 2026 Main. Code and data are available at https://github.com/AIDC-AI/Marco-LLM/tree/main/DetectRL-X

Whose Facts Win? LLM Source Preferences under Knowledge Conflicts

谁的事实获胜？在知识冲突下LLM的来源偏好

Jakob Schuster, Vagrant Gautam, Katja Markert

机构 * Heidelberg University（海德堡大学） ； Heidelberg Institute for Theoretical Studies（海德堡理论研究所） 纠错

专题命中 评测与基准 ：LLM(title,title_cn)；large language model(abstract)；language model(abstract)；分类 cs.CL

AI总结 研究探讨了LLM在知识冲突中的来源偏好，发现其更倾向于机构证实的信息，但可通过重复低可信度来源信息逆转。提出方法减少重复偏差，同时保持大部分偏好。

Comments Data and code: https://github.com/JaSchuste/llm-source-preference

Teaching a Large Language Model Tutor to Withhold the Answer: A Supervisor Architecture and an Evidence-Driven Method for Tuning Socratic Behavior

训练大型语言模型助教以保留答案：一种用于调整苏格拉底式行为的监督架构与证据驱动方法

Yusuf Pisan

专题命中 评测与基准 ：LLM(summary_cn,abstract)；large language model(title,abstract)；language model(title,abstract)

AI总结 本研究提出一种监督架构与证据驱动方法，训练LLM助教执行答案保留，通过自动评估调整苏格拉底式行为，使其在全部四个接受标准上达到完全合规。