语言大模型 / LLM

A Non-monotonic Self-terminating Language Model

Eugene Choi, Kyunghyun Cho, Cheolhyoung Lee

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Published as a conference paper at ICLR 2023

DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models

Zhengfu He, Tianxiang Sun, Kuanning Wang, Xuanjing Huang, Xipeng Qiu

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Work in progress. Code publicly available at https://github.com/Hzfinfdu/Diffusion-BERT

Discriminative Language Model as Semantic Consistency Scorer for Prompt-based Few-Shot Text Classification

Zhipeng Xie, Yahe Li

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments 21 pages, 3 figures

Can language models learn from explanations in context?

Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane X. Wang, Felix Hill

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Findings of EMNLP 2022

Do language models make human-like predictions about the coreferents of Italian anaphoric zero pronouns?

James A. Michaelov, Benjamin K. Bergen

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Accepted at COLING 2022

Learning Chess With Language Models and Transformers

Michael DeLeo, Erhan Guven

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Conference Paper

Disaster Tweets Classification using BERT-Based Language Model

Anh Duc Le

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments arXiv admin note: text overlap with arXiv:2102.12162 by other authors

Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs

Peter Hase, Mona Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, Srinivasan Iyer

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments 19 pages

Multi-task Language Modeling for Improving Speech Recognition of Rare Words

Chao-Han Huck Yang, Linda Liu, Ankur Gandhe, Yile Gu, Anirudh Raju, Denis Filimonov, Ivan Bulyko

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Accepted to IEEE Automatic Speech Recognition and Understanding (ASRU) 2021

StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling

Yikang Shen, Yi Tay, Che Zheng, Dara Bahri, Donald Metzler, Aaron Courville

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Published as a conference paper at ACL 2021

Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction

Taeuk Kim, Jihun Choi, Daniel Edmiston, Sang-goo Lee

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments ICLR 2020

Improve Language Modelling for Code Completion through Statement Level Language Model based on Statement Embedding Generated by BiLSTM

Yixiao Yang

专题命中 其他LLM ：language model(title,abstract)；SLM(abstract)

Comments The experimental data is not complete and has some error!

Do Massively Pretrained Language Models Make Better Storytellers?

Abigail See, Aneesh Pappu, Rohun Saxena, Akhila Yerukola, Christopher D. Manning

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Accepted to CoNLL 2019

Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition

Angli Liu, Jingfei Du, Veselin Stoyanov

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments NAACL 2019; updated to cite Zhou et al. (2018) EMNLP as a piece of related work

Character-Level Language Modeling with Deeper Self-Attention

Rami Al-Rfou, Dokook Choe, Noah Constant, Mandy Guo, Llion Jones

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments 8 pages, 7 figures

Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models

Takashi Wada, Tomoharu Iwata

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments 8 pages

Dependency Recurrent Neural Language Models for Sentence Completion

Piotr Mirowski, Andreas Vlachos

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Accepted for publication at ACL 2015

Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear

并非奇怪的爱：语言模型和生成语言理论比看起来更兼容

R. Thomas McCoy

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI

AI总结 本文探讨语言模型与生成语言理论的兼容性，提出LMs可支持基于形式结构的理论，扩展了可测试理论的范围，促进使用基础与生成理论的融合。

Comments Accepted to Behavioral and Brain Sciences; 4 pages; Commentary on "How Linguistics Learned to Stop Worrying and Love the Language Models" by Richard Futrell and Kyle Mahowald

GPT, But Backwards: Exactly Inverting Language Model Outputs

Adrians Skapars, Edoardo Manino, Youcheng Sun, Lucas C. Cordeiro

专题命中 其他LLM ：language model(title,abstract)；分类 cs.AI、cs.LG；foundation model(comments)

Comments 7 pages, ICML 2025 Workshop on Reliable and Responsible Foundation Models

Overview of the First Workshop on Language Models for Low-Resource Languages (LoResLM 2025)

Hansi Hettiarachchi, Tharindu Ranasinghe, Paul Rayson, Ruslan Mitkov, Mohamed Gaber, Damith Premasiri, Fiona Anting Tan, Lasitha Uyangodage

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI

Comments The First Workshop on Language Models for Low-Resource Languages (LoResLM 2025)

Ecosystem Graphs: The Social Footprint of Foundation Models

Rishi Bommasani, Dilara Soylu, Thomas I. Liao, Kathleen A. Creel, Percy Liang

专题命中 其他LLM ：foundation model(title,abstract)；分类 cs.AI、cs.LG

Comments Authored by the Center for Research on Foundation Models (CRFM) at the Stanford Institute for Human-Centered Artificial Intelligence (HAI). Ecosystem Graphs available at https://crfm.stanford.edu/ecosystem-graphs/

Journal ref Published in AIES 2024

Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research

Ross Gruetzemacher, David Paradice

专题命中 其他LLM ：language model(title,abstract)；分类 cs.CL、cs.AI

Comments Under review (revised once). Section 2, the literature review on deep transfer learning and transformer language models, is a valuable introduction for a broad audience (not just information systems researchers). 33 pages plus 13-page appendix

Does Listening Matter? Backchanneling and Nodding in AI Clone

倾听重要吗？AI克隆中的反馈式回应与点头动作

Koji Inoue, Kazushi Kato, Tatsuya Kawahara, Shunichi Kasahara

专题命中 其他LLM ：LLM(summary_cn,abstract)；分类 cs.CL

AI总结 本研究将口头反馈式回应与头部点头整合到配备语音克隆及LLM响应的AI克隆中，经35人被试内研究证实，添加互动倾听行为可提升AI克隆的感知专注度、真实感与共同在场感，其保真度需涵盖倾听行为。

Comments This paper has been accepted to the Late-Breaking Results (LBR) track of the 28th International Conference on Multimodal Interaction (ICMI 2026)

Why AI Detection Fails for Academic Integrity

为什么AI检测无法保障学术诚信

Jonathan A. Karr, Grigorii Khvatskii, Ting Hua, Nitesh V. Chawla

机构 * University of Notre Dame（圣母大学） 纠错

专题命中 其他LLM ：LLM(summary_cn,abstract)；分类 cs.LG

AI总结 该研究发现商业AI检测器无法区分AI编辑与LLM完整生成稿，轻度AI辅助润色的论文标记率远高于未修改稿，且人性化处理可大幅规避检测，表明检测器分数不能单独作为学术不端证据。

Comments Accepted to ACM AI Leadership Summit

ORBITER: Conflict-Aware Decision-Making for Agentic Last-Mile Delivery

ORBITER：面向智能体的最后一公里配送的冲突感知决策方法

Mingzhao Li, Chenxi Liu, Yan Zhao, Hao Miao

专题命中 其他LLM ：LLM(summary_cn,abstract)；分类 cs.AI

AI总结 本文针对最后一公里配送决策的可解释性与可靠性问题，提出ORBITER框架，结合LLM与结构化决策机制，在四城市数据上较最优基线平均提升9.2%，验证了方法的有效性。

Shared Circuits for Shared Grammar: Tracing Subject-Verb Agreement Across Languages

共享语法的共享电路：跨语言追踪主谓一致

Isabella Gidi, Antonio Almudévar, Core Francisco Park, Naomi Saphra, Ricard Marxer

机构 * Harvard University（哈佛大学） ； University of Zaragoza（萨拉戈萨大学） ； Boston University（波士顿大学） ； Univ Toulon（土伦大学） ； Aix Marseille Univ（艾克斯-马赛大学） ； CNRS（法国国家科学研究中心） ； LIS（信息科学实验室） ； ILLS（语言与语言科学研究所） 纠错

专题命中 其他LLM ：large language model(abstract,abstract_cn)；language model(abstract,abstract_cn)；分类 cs.CL

AI总结 本文研究多语言大语言模型的跨语言共享机制，以主谓一致为对象，通过29种语言的实验发现其复用部分共享计算结构，且与屈折语言的电路更相似。

Comments 25 pages including appendices, 16 figures. Accepted to COLM 2026

SeqFeed: Improving Agentic RTL Code Generation with Sequential Behavior Feedback

SeqFeed：通过顺序行为反馈改进智能体RTL代码生成

Yuxin Du, Juxin Niu, Tao Hu, Xi Wang, Zhe Jiang, Nan Guan

专题命中 其他LLM ：LLM(summary_cn,abstract)；分类 cs.CL

AI总结 针对智能体RTL代码生成的时序信息传递难题，本研究提出含SeQuery和SeGraph机制的SeqFeed，通过满足三项反馈要求提升LLM的RTL代码生成通过率。

ATLAS: Scaffold-Free Algorithm Synthesis by LLMs via Embedding-Guided Quality-Diversity Search

ATLAS：基于嵌入引导的质量多样性搜索的无支架大语言模型算法合成

Danial Yazdani, Mohammad Nabi Omidvar, Yuan Sun, Maksud Ibrahimov, Xiaodong Li

专题命中 其他LLM ：LLM(summary_cn,abstract)；分类 cs.AI

AI总结 ATLAS是基于LLM的无支架全算法合成框架，通过嵌入引导质量多样性搜索解决全算法设计空间问题，在NP难问题上优于相关基线，可保留不同区域的多类算法。

SkillCommit: Evolving Agent Skills through Behaviorally Validated Scope Expansion

SkillCommit：通过行为验证的范围扩展实现智能体技能演化

Yu He, Weikai Yang

专题命中 其他LLM ：LLM(abstract,abstract_cn)；large language model(abstract)；language model(abstract)；分类 cs.AI

AI总结 本研究提出SkillCommit框架，通过保留经验的验证行为、抽象相关技能，在RuleArena等数据集上提升智能体性能，且所学技能可跨模型迁移。

Do Personalized Skills Help Coding Agents? An Empirical Study of Developer Interaction Histories

个性化技能对编码智能体有帮助吗？开发者交互历史的实证研究

Shuyan Huang, Kai Du, Andrew Lan

专题命中 其他LLM ：LLM(abstract,abstract_cn)；large language model(abstract)；language model(abstract)；分类 cs.AI

AI总结 本研究通过对13位开发者的206个真实会话实验，发现从交互历史提炼的开发者个性化技能改进有限，而汇集自所有开发者的通用技能增益最大且最一致，为编码智能体的个性化策略提供了实证依据。

Comments 15 pages, 10 figures