语言大模型 / LLM

It's All Relative! -- A Synthetic Query Generation Approach for Improving Zero-Shot Relevance Prediction

Aditi Chaudhary, Karthik Raman, Michael Bendersky

专题命中 预训练与数据 ：large language model(abstract)；language model(abstract)；prompting(abstract)；分类 cs.CL

Comments 18 pages

Conversational AI Threads for Visualizing Multidimensional Datasets

Matt-Heun Hong, Anamaria Crisan

专题命中 预训练与数据 ：LLM(abstract)；large language model(abstract)；language model(abstract)；分类 cs.AI

Expressive TTS Driven by Natural Language Prompts Using Few Human Annotations

Hanglei Zhang, Yiwei Guo, Sen Liu, Xie Chen, Kai Yu

专题命中 预训练与数据 ：LLM(abstract)；large language model(abstract)；language model(abstract)；分类 cs.AI

Comments 5 pages,3 figures, submitted to ICASSP 2024

Modeling subjectivity (by Mimicking Annotator Annotation) in toxic comment identification across diverse communities

Senjuti Dutta, Sid Mittal, Sherol Chen, Deepak Ramachandran, Ravi Rajakumar, Ian Kivlichan, Sunny Mak, Alena Butryna, Praveen Paritosh

专题命中 预训练与数据 ：LLM(abstract)；large language model(abstract)；language model(abstract)；分类 cs.AI

CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering

Weiqi Wang, Tianqing Fang, Wenxuan Ding, Baixuan Xu, Xin Liu, Yangqiu Song, Antoine Bosselut

专题命中 预训练与数据 ：large language model(abstract)；language model(abstract)；pretraining(abstract)；分类 cs.CL

Comments Findings of EMNLP2023

Overview of Physics-Informed Machine Learning Inversion of Geophysical Data

Gerard T. Schuster, Shihang Feng

专题命中 预训练与数据 ：large language model(abstract)；language model(abstract)；pretraining(abstract)；分类 cs.LG

Comments 37 pages, 16 figures

What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization

Yufeng Zhang, Fengzhuo Zhang, Zhuoran Yang, Zhaoran Wang

专题命中 预训练与数据 ：large language model(abstract)；language model(abstract)；pretraining(abstract)；分类 cs.LG

On the Zero-Shot Generalization of Machine-Generated Text Detectors

Xiao Pu, Jingyu Zhang, Xiaochuang Han, Yulia Tsvetkov, Tianxing He

专题命中 预训练与数据 ：LLM(abstract)；large language model(abstract)；language model(abstract)；分类 cs.CL

"Tidy Up the Table": Grounding Common-sense Objective for Tabletop Object Rearrangement

Yiqing Xu, David Hsu

专题命中 预训练与数据 ：LLM(abstract)；large language model(abstract)；language model(abstract)；分类 cs.AI

Comments RSSLRL2023 Workshop, Under review for conference

ChatGPT for Arabic Grammatical Error Correction

Sang Yun Kwon, Gagan Bhatia, El Moatez Billah Nagoud, Muhammad Abdul-Mageed

专题命中 预训练与数据 ：large language model(abstract)；language model(abstract)；prompting(abstract)；分类 cs.AI

Augmenting Greybox Fuzzing with Generative AI

Jie Hu, Qian Zhang, Heng Yin

专题命中 预训练与数据 ：LLM(abstract)；large language model(abstract)；language model(abstract)；分类 cs.AI

Comments 20 pages, 11 figures

Guided scenarios with simulated expert personae: a remarkable strategy to perform cognitive work

David Van Buren

专题命中 预训练与数据 ：LLM(abstract)；large language model(abstract)；language model(abstract)；分类 cs.AI

Comments 11 pages, 3 figures, 1 listing

PaLI: A Jointly-Scaled Multilingual Language-Image Model

Xi Chen, Xiao Wang, Soravit Changpinyo, AJ Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Nan Ding, Keran Rong, Hassan Akbari, Gaurav Mishra, Linting Xue, Ashish Thapliyal, James Bradbury, Weicheng Kuo, Mojtaba Seyedhosseini, Chao Jia, Burcu Karagol Ayan, Carlos Riquelme, Andreas Steiner, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut

专题命中 预训练与数据 ：large language model(abstract)；language model(abstract)；pretraining(abstract)；分类 cs.CL

Comments ICLR 2023 (Notable-top-5%)

Code4Struct: Code Generation for Few-Shot Event Structure Prediction

Xingyao Wang, Sha Li, Heng Ji

专题命中 预训练与数据 ：LLM(abstract)；large language model(abstract)；language model(abstract)；分类 cs.CL

Comments ACL 2023

"Genlangs" and Zipf's Law: Do languages generated by ChatGPT statistically look human?

Justin Diamond

专题命中 预训练与数据 ：LLM(abstract)；large language model(abstract)；language model(abstract)；分类 cs.CL

Comments 10 pages, 7 figures, link to text corpora and source code available

A Prompt Log Analysis of Text-to-Image Generation Systems

Yutong Xie, Zhaoying Pan, Jinge Ma, Luo Jie, Qiaozhu Mei

专题命中 预训练与数据 ：LLM(abstract)；large language model(abstract)；language model(abstract)；分类 cs.AI

The Learnability of In-Context Learning

Noam Wies, Yoav Levine, Amnon Shashua

专题命中 预训练与数据 ：large language model(abstract)；language model(abstract)；pretraining(abstract)；分类 cs.CL

Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data

Tim Jansen, Yangling Tong, Victoria Zevallos, Pedro Ortiz Suarez

专题命中 预训练与数据 ：large language model(abstract)；language model(abstract)；small language model(abstract)；分类 cs.CL

Comments 14 pages, 2 figures

Multilingual Sequence-to-Sequence Models for Hebrew NLP

Matan Eyal, Hila Noga, Roee Aharoni, Idan Szpektor, Reut Tsarfaty

专题命中 预训练与数据 ：large language model(abstract)；language model(abstract)；pretraining(abstract)；分类 cs.CL

Training Naturalized Semantic Parsers with Very Little Data

Subendhu Rongali, Konstantine Arkoudas, Melanie Rubino, Wael Hamza

专题命中 预训练与数据 ：large language model(abstract)；language model(abstract)；pretraining(abstract)；分类 cs.CL

Comments IJCAI 2022

FLAVA: A Foundational Language And Vision Alignment Model

Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, Douwe Kiela

专题命中 预训练与数据 ：language model(abstract)；foundation model(abstract)；pretraining(abstract)；分类 cs.CL

Comments CVPR 2022

Analyzing Commonsense Emergence in Few-shot Knowledge Models

Jeff Da, Ronan Le Bras, Ximing Lu, Yejin Choi, Antoine Bosselut

专题命中 预训练与数据 ：large language model(abstract)；language model(abstract)；pretraining(abstract)；分类 cs.CL

Comments AKBC 2021

KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation

Yiran Xing, Zai Shi, Zhao Meng, Gerhard Lakemeyer, Yunpu Ma, Roger Wattenhofer

专题命中 预训练与数据 ：large language model(abstract)；language model(abstract)；pretraining(abstract)；分类 cs.CL

Comments ACL-IJCNLP 2021 main conference. The first three authors contribute equally to this work

Evolution Is All You Need: Phylogenetic Augmentation for Contrastive Learning

Amy X. Lu, Alex X. Lu, Alan Moses

专题命中 预训练与数据 ：large language model(abstract)；language model(abstract)；pretraining(abstract)；分类 cs.LG

Comments Machine Learning in Computational Biology (MLCB) 2020

Low-Resource Languages Jailbreak GPT-4

Zheng-Xin Yong, Cristina Menghini, Stephen H. Bach

专题命中 预训练与数据 ：language model(abstract,comments)；large language model(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR) 2023. Best Paper Award

Belief Propagation in LLM World Models: Measuring Strategic Information Bias with Prediction Markets

大语言模型世界模型中的信念传播：用预测市场测量战略信息偏差

Mykola Khandoga, Yevhen Kostiuk, Anton Polishko, Yurii Filipchuk, Kostiantyn Kozlov, Dmytro Zamriy, Artur Kiulian

机构 * Future Principle（未来原理） ； Aarhus University（奥胡斯大学） 纠错

专题命中 预训练与数据 ：LLM(title)；分类 cs.CL、cs.LG

AI总结 研究大语言模型结合预测市场测量信息偏差，通过消融特定文本偏差贡献，应用于乌克兰相关预测市场发现英语新闻背景致偏差，主要源于文本，补充乌军事分析源可减偏差，此偏差在多架构中会持续并影响下游决策。

Comments Accepted at UNLP 2026. 12 pages, 8 figures, 11 tables. Dataset available at https://huggingface.co/datasets/OpenBabylon/unlp-ukraine-forecasting

Compact Example-Based Explanations for Language Models

紧凑的基于示例的语言模型解释

Loris Schoenegger, Benjamin Roth

机构 * Faculty of Computer Science, University of Vienna（维也纳大学计算机科学学院） ； UniVie Doctoral School Computer Science, University of Vienna（维也纳大学计算机科学博士学院） ； Faculty of Philological and Cultural Studies, University of Vienna（维也纳大学语言文化研究学院） 纠错

专题命中 预训练与数据 ：language model(title)；分类 cs.CL、cs.LG

AI总结 本文提出一种无需重新训练的选例相关性评分，用于评估示例集对模型输出解释的有效性，并展示了其在选择策略中的应用，提升解释质量。

Comments ACL 2026 Findings. 9 pages

GPC: Large-Scale Generative Pretraining for Transferable Motor Control

GPC：面向可迁移运动控制的大规模生成式预训练

Yi Shi, Yifeng Jiang, Chen Tessler, Xue Bin Peng

专题命中 预训练与数据 ：pretraining(title)；分类 cs.AI、cs.LG

AI总结 提出生成式预训练控制器（GPC），通过令牌化和下一令牌建模从大规模运动数据集中学习通用控制器，结合有限标量量化（FSQ）和自回归Transformer，实现高成功率运动复现和鲁棒的下游适应。

Journal ref SIGGRAPH 2026 Conference Proceedings

ParaSpeechCLAP: A Dual-Encoder Speech-Text Model for Rich Stylistic Language-Audio Pretraining

ParaSpeechCLAP：面向丰富风格语言-音频预训练的双编码器语音-文本模型

Anuj Diwan, Eunsol Choi, David Harwath

机构 * Department of Computer Science, The University of Texas at Austin, USA（德克萨斯大学奥斯汀分校计算机科学系） ； Computer Science and Data Science, New York University, USA（纽约大学计算机科学与数据科学系） 纠错

专题命中 预训练与数据 ：pretraining(title)；分类 cs.CL、cs.AI

AI总结 提出ParaSpeechCLAP双编码器模型，将语音与文本风格描述映射到共享嵌入空间，支持丰富内在和情境风格描述，在风格描述检索、属性分类及TTS奖励模型中优于基线。

Comments Interspeech 2026

Spokes: Optimizing for Diverse Pretraining Data Selection

Spokes: 优化多样化预训练数据选择

Clarence Lee, Yejin Choi, Luke Zettlemoyer, Pang Wei Koh, Hai Leong Chieu

机构 * DSO National Laboratories（DSO国家实验室） ； Stanford University（斯坦福大学） ； University of Washington（华盛顿大学） 纠错

专题命中 预训练与数据 ：pretraining(title)；分类 cs.CL、cs.AI

AI总结 提出基于G-Vendi分数的概率多样化框架，通过指数梯度下降直接优化数据多样性，在FineWeb和DCLM上提升下游性能1.5和1.4个点。

Comments 9 pages, 4 figures