语言大模型 / LLM

ChatGPT-Crawler: Find out if ChatGPT really knows what it's talking about

Aman Rangapur, Haoran Wang

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

Comments 9 pages, 1 figure, 4 tables

Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System

Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, Jiawei Zhang

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

Two-stage Pipeline for Multilingual Dialect Detection

Ankit Vaidya, Aditya Kane

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

GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as A Plug-and-Play Transductive Model for Medical Image Analysis

Yizhe Zhang, Danny Z. Chen

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

Comments Version 3: Added appendix with more results and visualizations. Questions and suggestions are welcome

FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering

Qichen Ye, Bowen Cao, Nuo Chen, Weiyuan Xu, Yuexian Zou

专题命中 领域大模型 ：language model(abstract)；post-training(abstract)；分类 cs.CL、cs.LG

Comments Accepted in AAAI 2023, oral

How GPT-3 responds to different publics on climate change and Black Lives Matter: A critical appraisal of equity in conversational AI

Kaiping Chen, Anqi Shao, Jirayu Burapacheep, Yixuan Li

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

PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents

Weixiong Lin, Ziheng Zhao, Xiaoman Zhang, Chaoyi Wu, Ya Zhang, Yanfeng Wang, Weidi Xie

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

Comments 10 pages, 3 figures

SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains

Koustava Goswami, Lukas Lange, Jun Araki, Heike Adel

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

Comments Accepted at EACL 2023 Main Conference

A Multi-View Joint Learning Framework for Embedding Clinical Codes and Text Using Graph Neural Networks

Lecheng Kong, Christopher King, Bradley Fritz, Yixin Chen

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

ChatGPT: The End of Online Exam Integrity?

Teo Susnjak

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

Prompt Learning for Domain Adaptation in Task-Oriented Dialogue

Makesh Narsimhan Sreedhar, Christopher Parisien

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

Comments Accepted for publication at SereTOD Workshop - EMNLP 2022

Ontology-Driven Self-Supervision for Adverse Childhood Experiences Identification Using Social Media Datasets

Jinge Wu, Rowena Smith, Honghan Wu

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

Comments arXiv admin note: text overlap with arXiv:2208.11466

A No-Code Low-Code Paradigm for Authoring Business Automations Using Natural Language

Michael Desmond, Evelyn Duesterwald, Vatche Isahagian, Vinod Muthusamy

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

Task Transfer and Domain Adaptation for Zero-Shot Question Answering

Xiang Pan, Alex Sheng, David Shimshoni, Aditya Singhal, Sara Rosenthal, Avirup Sil

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

Comments NAACL 2022 Deep Learning for Low-Resource NLP Workshop Paper

Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired Classifiers

Payam Karisani

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

Comments AAAI 2022

Transformer-based Approaches for Legal Text Processing

Ha-Thanh Nguyen, Minh-Phuong Nguyen, Thi-Hai-Yen Vuong, Minh-Quan Bui, Minh-Chau Nguyen, Tran-Binh Dang, Vu Tran, Le-Minh Nguyen, Ken Satoh

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

Comments arXiv admin note: substantial text overlap with arXiv:2106.13405

PSG: Prompt-based Sequence Generation for Acronym Extraction

Bin Li, Fei Xia, Yixuan Weng, Xiusheng Huang, Bin Sun, Shutao Li

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

Comments Accepted for Artificial Intelligence on Scientific Document Understanding (SDU) workshop at AAAI 2022

Attention Augmented Convolutional Transformer for Tabular Time-series

Sharath M Shankaranarayana, Davor Runje

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

On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation

Dan Iter, David Grangier

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

Comparing the Performance of NLP Toolkits and Evaluation measures in Legal Tech

Muhammad Zohaib Khan

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

Large Scale Legal Text Classification Using Transformer Models

Zein Shaheen, Gerhard Wohlgenannt, Erwin Filtz

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

Challenge Closed-book Science Exam: A Meta-learning Based Question Answering System

Xinyue Zheng, Peng Wang, Qigang Wang, Zhongchao Shi

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

Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling

Xiaochuang Han, Jacob Eisenstein

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

Comments EMNLP 2019

Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems

立场：科学团队中的智能体应作为人机系统（HAS）进行研究

Patrick Emami, Sameera Horawalavithana, Truc Nguyen, Gihan Panapitiya, Bruno Jacob, Siddhisanket Raskar, Saumya Sinha, Jared D. Willard, Andrew Glaws, Nithin Somasekharan, Ling Yue, Brian Lu, Shaowu Pan, Jason Eisner

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

AI总结 针对当前AI科学家研究忽视科学团队社会层面的问题，提出将其作为人机系统（HAS）研究，分析相关风险并呼吁开发人机协同数学框架。

Comments 15 pages. Accepted at the COLM 2nd Workshop on Language Models for Scientific Discovery

Who Trusts AI with Their Emotions? Trust Formation and Sociodemographic Variation in LLM Use for Emotional Support

谁会信任AI来处理自己的情绪？情感支持大语言模型使用中的信任形成与社会人口统计学差异

Natalia Amat-Lefort, Mert Yazan, Amanda Cercas Curry, Flor Miriam Plaza-del-Arco

专题命中 领域大模型 ：LLM(title)

AI总结 本研究针对情感支持大语言模型，开发验证了心理测量量表，通过结构方程模型与多组分析发现不同社会人口群体对AI的信任影响因素及采纳逻辑存在差异，为情感支持AI的公平设计提供了理论支撑。

Where Facts Go Missing: A Layerwise Taxonomy and Per-Layer Attribution of Information Omission in Air-Gapped LLMAgent Pipelines

事实缺失的地方：气隙式大语言模型智能体管道中信息遗漏的分层分类和逐层归因

Santhiya Rajan, Samuel Mugel, Roman Orus

专题命中 领域大模型 ：LLM(title)

AI总结 研究气隙式大语言模型智能体管道中信息遗漏问题，提出九层分类法、归因方法、跨架构比较框架及运行时检测框架，经多模型多引擎试验，确定遗漏率及来源，为定位运营商干预位置提供依据。

Longitudinal 3D Foundation Modeling for Neoadjuvant Breast Cancer Response Prediction from Serial DCE-MRI

用于从系列动态对比增强磁共振成像（DCE-MRI）预测乳腺癌新辅助治疗反应的纵向三维基础建模

Fidel Omar Tito Cruz, Neda Ghafouri, Zengyan Wang, Pegah Khosravi, Yu Tian, Chen Chen

机构 * University of Central Florida（中佛罗里达大学） 纠错

专题命中 领域大模型 ：foundation model(title)

AI总结 本研究提出结合三维基础编码器与时间动态网络的纵向框架，融合系列DCE-MRI与临床数据，在982例乳腺癌患者上实现了较好的pCR预测性能。

Comments Accepted at the Applications of Medical AI (AMAI) Workshop at MICCAI 2026

PRIMA: Pre-Training with Risk-Integrated Image--Metadata Alignment for Medical Diagnosis with LLM-Based Feature Aggregation

PRIMA：通过大语言模型进行风险集成图像-元数据对齐的医学诊断预训练

Yiqing Wang, Chunming He, Ziyun Yang, Maria Woodward, Ming-Chen Lu, Mercy Pawar, Leslie Niziol, Sina Farsiu

机构 * Department of Biomedical Engineering, Duke University（杜克大学生物医学工程系） ； Department of Ophthalmology and Visual Sciences, University of Michigan（密歇根大学眼科与视觉科学系） 纠错

专题命中 领域大模型 ：LLM(title)

AI总结 提出PRIMA框架，通过检索增强生成策展风险-疾病关联专家语料库优化文本编码器，用双编码器预训练策略及四个互补损失函数弥合模态差距，融合特征用于疾病分类，性能优于其他方法。

Industrial Practice of LLM-Based Test Case Carving and Assertion Generation (Experience Paper)

基于大语言模型的测试用例提取与断言生成的工业实践（经验论文）

Haozhen You, Zhen Dong, Jingjing Wang, Qiang Li, Xin Peng

专题命中 领域大模型 ：LLM(title)

AI总结 针对微服务系统回归测试因文档问题面临的挑战，提出NL2Test工具，通过自然语言描述和流量捕获生成可执行API回归测试，经大语言模型和确定性算法完成测试用例提取与断言生成等任务，在工业场景中效果良好，减少人工并提升自动化程度。

Comments 23 pages, 6 figures, Proceedings of the 35th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2026)

Beyond Fixed Depths and Widths: Optimizing Textual Decoding Tries in LLM-based Generative Recommendation

超越固定深度和宽度：优化基于大语言模型的生成式推荐中的文本解码树

Jingzhe Liu, Hanbing Wang, Jiliang Tang, Liam Collins, Tong Zhao, Neil Shah, Mingxuan Ju

专题命中 领域大模型 ：LLM(title)

AI总结 研究基于大语言模型的生成式推荐中解码树结构问题，提出BONSAI框架，从项目元数据提取信息丰富的词，用最小集覆盖公式构建满足自适应ID长度和约束分支因子属性的解码树，实验显示比基线有高达21.6%的相对改进。