语言大模型 / LLM

Investigating Interaction Modes and User Agency in Human-LLM Collaboration for Domain-Specific Data Analysis

Jiajing Guo, Vikram Mohanty, Jorge Piazentin Ono, Hongtao Hao, Liang Gou, Liu Ren

专题命中 领域大模型 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)

Comments CHI'24 Late-Breaking Work

Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology Imaging

Poojitha Thota, Jai Prakash Veerla, Partha Sai Guttikonda, Mohammad S. Nasr, Shirin Nilizadeh, Jacob M. Luber

专题命中 领域大模型 ：language model(title,abstract)；foundation model(abstract)；pretraining(abstract)

Automated Social Science: Language Models as Scientist and Subjects

Benjamin S. Manning, Kehang Zhu, John J. Horton

专题命中 领域大模型 ：language model(title,abstract)；LLM(abstract)；large language model(abstract)

Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain

Iker García-Ferrero, Rodrigo Agerri, Aitziber Atutxa Salazar, Elena Cabrio, Iker de la Iglesia, Alberto Lavelli, Bernardo Magnini, Benjamin Molinet, Johana Ramirez-Romero, German Rigau, Jose Maria Villa-Gonzalez, Serena Villata, Andrea Zaninello

专题命中 领域大模型 ：LLM(title)；large language model(abstract)；language model(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments LREC-COLING 2024

CopilotCAD: Empowering Radiologists with Report Completion Models and Quantitative Evidence from Medical Image Foundation Models

Sheng Wang, Tianming Du, Katherine Fischer, Gregory E Tasian, Justin Ziemba, Joanie M Garratt, Hersh Sagreiya, Yong Fan

专题命中 领域大模型 ：foundation model(title,abstract)；large language model(abstract)；language model(abstract)

Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation

Haojie Zhang, Yongyi Su, Xun Xu, Kui Jia

专题命中 领域大模型 ：foundation model(title,abstract)；large language model(abstract)；language model(abstract)

Comments 20 pages, 12 figures

CoQuest: Exploring Research Question Co-Creation with an LLM-based Agent

Yiren Liu, Si Chen, Haocong Cheng, Mengxia Yu, Xiao Ran, Andrew Mo, Yiliu Tang, Yun Huang

专题命中 领域大模型 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)

Comments Accepted to SIGCHI 2024

MedLM: Exploring Language Models for Medical Question Answering Systems

Niraj Yagnik, Jay Jhaveri, Vivek Sharma, Gabriel Pila

专题命中 领域大模型 ：language model(title,abstract)；large language model(abstract)；分类 cs.CL、cs.AI、cs.LG

Learning Agent-based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chat

John Chen, Xi Lu, Michael Rejtig, David Du, Ruth Bagley, Michael S. Horn, Uri J. Wilensky

专题命中 领域大模型 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)

Comments Conditionally accepted (with minor revisions) by Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24)

GraphiMind: LLM-centric Interface for Information Graphics Design

Qirui Huang, Min Lu, Joel Lanir, Dani Lischinski, Daniel Cohen-Or, Hui Huang

专题命中 领域大模型 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)

User Embedding Model for Personalized Language Prompting

Sumanth Doddapaneni, Krishna Sayana, Ambarish Jash, Sukhdeep Sodhi, Dima Kuzmin

专题命中 领域大模型 ：prompting(title,abstract)；language model(abstract)；分类 cs.CL、cs.AI、cs.LG

Clinical Text Deduplication Practices for Efficient Pretraining and Improved Clinical Tasks

Isotta Landi, Eugenia Alleva, Alissa A. Valentine, Lauren A. Lepow, Alexander W. Charney

专题命中 领域大模型 ：pretraining(title,abstract)；language model(abstract)；分类 cs.CL、cs.AI、cs.LG

Bridging the Information Gap Between Domain-Specific Model and General LLM for Personalized Recommendation

Wenxuan Zhang, Hongzhi Liu, Yingpeng Du, Chen Zhu, Yang Song, Hengshu Zhu, Zhonghai Wu

专题命中 领域大模型 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)

LLM-based Frameworks for Power Engineering from Routine to Novel Tasks

Ran Li, Chuanqing Pu, Junyi Tao, Canbing Li, Feilong Fan, Yue Xiang, Sijie Chen

专题命中 领域大模型 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)

On the Impact of Cross-Domain Data on German Language Models

Amin Dada, Aokun Chen, Cheng Peng, Kaleb E Smith, Ahmad Idrissi-Yaghir, Constantin Marc Seibold, Jianning Li, Lars Heiliger, Xi Yang, Christoph M. Friedrich, Daniel Truhn, Jan Egger, Jiang Bian, Jens Kleesiek, Yonghui Wu

专题命中 领域大模型 ：language model(title,abstract)；large language model(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments 13 pages, 1 figure, accepted at Findings of the Association for Computational Linguistics: EMNLP 2023

SpeechGen: Unlocking the Generative Power of Speech Language Models with Prompts

Haibin Wu, Kai-Wei Chang, Yuan-Kuei Wu, Hung-yi Lee

专题命中 领域大模型 ：language model(title,abstract)；large language model(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Work in progress. The first three authors contributed equally

Enhancing Job Recommendation through LLM-based Generative Adversarial Networks

Yingpeng Du, Di Luo, Rui Yan, Hongzhi Liu, Yang Song, Hengshu Zhu, Jie Zhang

专题命中 领域大模型 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)

Comments 13 pages, 6 figures, 3 tables

An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code

Max Hort, Anastasiia Grishina, Leon Moonen

专题命中 领域大模型 ：language model(title,abstract)；large language model(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Accepted for publication in the 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023)

Med-MMHL: A Multi-Modal Dataset for Detecting Human- and LLM-Generated Misinformation in the Medical Domain

Yanshen Sun, Jianfeng He, Shuo Lei, Limeng Cui, Chang-Tien Lu

专题命中 领域大模型 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)

Language Models are Few-shot Learners for Prognostic Prediction

Zekai Chen, Mariann Micsinai Balan, Kevin Brown

专题命中 领域大模型 ：language model(title,abstract)；large language model(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments 7 pages, 5 figures, 5 tables

BudgetLongformer: Can we Cheaply Pretrain a SotA Legal Language Model From Scratch?

Joel Niklaus, Daniele Giofré

专题命中 领域大模型 ：language model(title,abstract)；pretraining(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Accepted at ENLSP @ NeurIPS 2022

What Language Model to Train if You Have One Million GPU Hours?

Teven Le Scao, Thomas Wang, Daniel Hesslow, Lucile Saulnier, Stas Bekman, M Saiful Bari, Stella Biderman, Hady Elsahar, Niklas Muennighoff, Jason Phang, Ofir Press, Colin Raffel, Victor Sanh, Sheng Shen, Lintang Sutawika, Jaesung Tae, Zheng Xin Yong, Julien Launay, Iz Beltagy

专题命中 领域大模型 ：language model(title,abstract)；large language model(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Findings of EMNLP 2022

Foundation Model for Cardiac Time Series via Masked Latent Attention

通过掩码潜在注意力的心脏时间序列基础模型

Moritz Vandenhirtz, Samuel Ruipérez-Campillo, Simon Böhi, Sonia Laguna, Irene Cannistraci, Andrea Agostini, Ece Ozkan, Thomas M. Sutter, Julia E. Vogt

机构 * Department of Computer Science, ETH Zurich, Switzerland（苏黎世联邦理工学院计算机科学系，瑞士） ； Department of Biomedical Engineering, University of Basel, Switzerland（巴塞尔大学生物医学工程系，瑞士） 纠错

专题命中 领域大模型 ：foundation model(title,abstract)；pretraining(abstract)；分类 cs.AI、cs.LG

AI总结 本文提出LAMAE基础模型，通过自监督预训练学习跨导联连接机制，提升ECG表示质量和迁移能力，在预测ICD-10编码上优于独立导联掩码模型和对齐基线。

Comments First two authors are co-first. Last two authors are co-senior

Journal ref ICLR 2026: Workshop on Foundation Models for Science

AEGIS: Preventing Cross-Domain Resource Abuse in MCP

AEGIS：防止MCP中的跨域资源滥用

Shriti Priya, Teryl Taylor, Frederico Araujo

专题命中 领域大模型 ：LLM(summary_cn,abstract_cn)；large language model(abstract)；language model(abstract)；分类 cs.AI

AI总结 本文提出AEGIS，该组件借助LLM将MCP工具调用归一化为统一表示，结合Open Policy Agent与ContextForge AI Gateway，可防止跨异构MCP工具和模态的资源滥用。

Historical Backtesting for Scientific Question Discovery: A Protocol and Astronomy Pilot

用于科学问题发现的历史回溯测试：协议与天文学试点

Hui Mao

专题命中 领域大模型 ：LLM(summary_cn,abstract)；prompting(abstract)；分类 cs.AI

AI总结 该研究提出可证伪的历史回溯测试协议评估科学问题生成系统，发布天文学实例验证发现，优先证据结构生成优于仅LLM提示，还指出结果分类问题，发布前瞻性无干扰测试实例。

Comments 27 pages, 10 tables. Benchmark, code, frozen instances, and the prospective 2026 submission: https://github.com/nonameisready/scientific-question-discovery-benchmark

On Measuring Semantic Preservation in Legal Ontology Learning

论法律本体学习中的语义保留度量

Albert Sadowski, Jarosław A. Chudziak

机构 * Warsaw University of Technology（华沙理工大学） 纠错

专题命中 领域大模型 ：LLM(summary_cn,abstract)；language model(abstract)；分类 cs.CL

AI总结 本文针对法律本体学习的语义保留评估问题，提出通过对比LLM在源文档与转换后表示上的任务性能量化语义损失，经多模型多方法实验发现语义损失随模型-方法配对显著变化，为法律知识系统配置选择提供指导。

Comments Accepted for publication at the 30th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2026)

Eleven Years of BRACIS: A Meta-Scientific Study of the Brazilian Conference on Intelligent Systems

BRACIS 十一年：巴西智能系统会议的元科学研究

Thales Sales Almeida, Giovana Kerche Bonás, Thiago Laitz, João Guilherme Alves Santos, Hugo Abonizio, Roseval Malaquias Junior, Marcos Piau, Celio Larcher, Ramon Pires, Rodrigo Nogueira

专题命中 领域大模型 ：LLM(summary_cn,abstract_cn)；large language model(abstract)；language model(abstract)；分类 cs.AI

AI总结 本研究对2015-2025年BRACIS的1066篇录用论文进行元科学分析，揭示其研究领域变化、作者与机构分布、引用特征及开放实践情况，发现LLM研究占比提升、引用集中、预印本与高引用相关等结论。

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning

RISE-RL：基于评分规则的开放式强化学习选择性探索

Jinkun Hou, Zhuo Liu, Huimin Ren, Hongsheng Xin, Pan Zhou, Kun Zhan

机构 * Li Auto Inc.（理想汽车） 纠错

专题命中 领域大模型 ：LLM(summary_cn,abstract_cn)；large language model(abstract)；language model(abstract)；分类 cs.AI

AI总结 该研究针对开放式任务中LLM的强化学习探索难题，提出RISE-RL方法，通过奖励过滤与策略支持塑造实现选择性内化，在多领域基准评测中显著优于现有方法。

Contemporary AI lacks the imagination to diverge or negate in science

当代人工智能缺乏在科学中发散或否定的想象力

Honglin Bao, Siyang Wu, Xiao Liu, Sida Li, Shiyun Cao, James A. Evans

机构 * Data Science Institute, University of Chicago（芝加哥大学数据科学研究所） ； Knowledge Lab, University of Chicago（芝加哥大学知识实验室） 纠错

专题命中 领域大模型 ：LLM(abstract,abstract_cn)；large language model(abstract)；language model(abstract)；prompting(abstract)

AI总结 通过大规模科学家评估，发现当前AI在科学假设生成中缺乏多样性，无法自发提出零假设，且自动评估与专家判断一致性低，但微调奖励模型可缩小差距。

MARS: Multi-Agent Re-ranking for Repeat-Order Food Delivery Recommendation

MARS：用于重复订单食品配送推荐的多智能体重排

Jiahao Tian, Zhenkai Wang

专题命中 领域大模型 ：LLM(abstract,abstract_cn)；large language model(abstract)；language model(abstract)；prompting(abstract)

AI总结 研究针对重复订单食品配送推荐，提出MARS模块化多智能体重排框架，分两阶段推荐，结合多种信号与推理。通过实验评估，贡献包括给出集成框架、证明预训练主干结合轻量级检索有竞争力、建立可重现评估设置。