语言大模型 / LLM

Whose Name Comes Up? Auditing LLM-Based Scholar Recommendations

Daniele Barolo, Chiara Valentin, Fariba Karimi, Luis Galárraga, Gonzalo G. Méndez, Lisette Espín-Noboa

专题命中 知识编辑与模型理解 ：LLM(title)；分类 cs.AI

Comments 40 pages: 10 main (incl. 9 figures), 3 references, and 27 appendix. Paper under-review

Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding

Chuwei Luo, Guozhi Tang, Qi Zheng, Cong Yao, Lianwen Jin, Chenliang Li, Yang Xue, Luo Si

机构 * Alibaba Group（阿里巴巴集团） ； School of Electronic and Information Engineering（电子与信息工程学院） ； South China University of Technology（华南理工大学） 纠错

专题命中 知识编辑与模型理解 ：language model(title)；分类 cs.CL

Comments IJDAR 2025

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering

Chaitanya Ravuri, Saman Amarasinghe

机构 * Massachusetts Institute of Technology（麻省理工学院） 纠错

专题命中 知识编辑与模型理解 ：LLM(title)；分类 cs.AI

Comments 9 pages, 1 figure

SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model

Zhao Yang, Jiwei Zhu, Bing Su

专题命中 知识编辑与模型理解 ：foundation model(title)；分类 cs.LG

Comments Accepted to ICML 2025

HalluCounter: Reference-free LLM Hallucination Detection in the Wild!

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

机构 * IIIT Hyderabad（IIIT海得拉巴） ； TCS Research, Hyderabad, India（TCS研究, 海得拉巴, 印度） ； University of Oslo, Norway（奥斯陆大学） 纠错

专题命中 知识编辑与模型理解 ：LLM(title)；分类 cs.CL

Comments 30 pages, 3 figures

Are you SURE? Enhancing Multimodal Pretraining with Missing Modalities through Uncertainty Estimation

Duy A. Nguyen, Quan Huu Do, Khoa D. Doan, Minh N. Do

专题命中 知识编辑与模型理解 ：pretraining(title)；分类 cs.LG

Feedback-Enhanced Hallucination-Resistant Vision-Language Model for Real-Time Scene Understanding

Zahir Alsulaimawi

专题命中 知识编辑与模型理解 ：language model(title)；分类 cs.LG

Multimodal Data Integration for Sustainable Indoor Gardening: Tracking Anyplant with Time Series Foundation Model

Seyed Hamidreza Nabaei, Zeyang Zheng, Dong Chen, Arsalan Heydarian

专题命中 知识编辑与模型理解 ：foundation model(title)；分类 cs.LG

Comments Accepted at ASCE International Conference on Computing in Civil Engineering (i3ce)

The Interaction Layer: An Exploration for Co-Designing User-LLM Interactions in Parental Wellbeing Support Systems

Sruthi Viswanathan, Seray Ibrahim, Ravi Shankar, Reuben Binns, Max Van Kleek, Petr Slovak

专题命中 知识编辑与模型理解 ：LLM(title)；分类 cs.AI

Word Boundary Information Isn't Useful for Encoder Language Models

Edward Gow-Smith, Dylan Phelps, Harish Tayyar Madabushi, Carolina Scarton, Aline Villavicencio

专题命中 知识编辑与模型理解 ：language model(title)；分类 cs.CL

Comments 9th Workshop on Representation Learning for NLP

Getting More Juice Out of Your Data: Hard Pair Refinement Enhances Visual-Language Models Without Extra Data

Haonan Wang, Minbin Huang, Runhui Huang, Lanqing Hong, Hang Xu, Tianyang Hu, Xiaodan Liang, Zhenguo Li, Hong Cheng, Kenji Kawaguchi

专题命中 知识编辑与模型理解 ：language model(title)；分类 cs.CL

Comments Accepted to NAACL 2025, main conference. 20 pages, 10 figures, 10 tables

Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge

Li Zhou, Taelin Karidi, Wanlong Liu, Nicolas Garneau, Yong Cao, Wenyu Chen, Haizhou Li, Daniel Hershcovich

专题命中 知识编辑与模型理解 ：large language model(abstract,comments)；language model(abstract,comments)；分类 cs.CL

Comments cultural bias analysis, cultural knowledge probing, large language models, cultural NLP; Accepted by NAACL2025

Journal ref NAACL2025

PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental Learning

Xianghu Yue, Yiming Chen, Xueyi Zhang, Xiaoxue Gao, Mengling Feng, Mingrui Lao, Huiping Zhuang, Haizhou Li

专题命中 知识编辑与模型理解 ：prompting(title)；分类 cs.LG

The Design of an LLM-powered Unstructured Analytics System

Eric Anderson, Jonathan Fritz, Austin Lee, Bohou Li, Mark Lindblad, Henry Lindeman, Alex Meyer, Parth Parmar, Tanvi Ranade, Mehul A. Shah, Benjamin Sowell, Dan Tecuci, Vinayak Thapliyal, Matt Welsh

专题命中 知识编辑与模型理解 ：LLM(title)；分类 cs.AI

Comments Included in the proceedings of The Conference on Innovative Data Systems Research (CIDR) 2025

Large Concept Models: Language Modeling in a Sentence Representation Space

LCM team, Loïc Barrault, Paul-Ambroise Duquenne, Maha Elbayad, Artyom Kozhevnikov, Belen Alastruey, Pierre Andrews, Mariano Coria, Guillaume Couairon, Marta R. Costa-jussà, David Dale, Hady Elsahar, Kevin Heffernan, João Maria Janeiro, Tuan Tran, Christophe Ropers, Eduardo Sánchez, Robin San Roman, Alexandre Mourachko, Safiyyah Saleem, Holger Schwenk

专题命中 知识编辑与模型理解 ：language model(title)；分类 cs.CL

Comments 49 pages

Attend First, Consolidate Later: On the Importance of Attention in Different LLM Layers

Amit Ben-Artzy, Roy Schwartz

专题命中 知识编辑与模型理解 ：LLM(title)；分类 cs.CL

A Masked language model for multi-source EHR trajectories contextual representation learning

Ali Amirahmadi, Mattias Ohlsson, Kobra Etminani, Olle Melander, Jonas Björk

专题命中 知识编辑与模型理解 ：language model(title)；分类 cs.LG

Comments Presented at Proceedings of MIE 2023

Prompting Hard or Hardly Prompting: Prompt Inversion for Text-to-Image Diffusion Models

Shweta Mahajan, Tanzila Rahman, Kwang Moo Yi, Leonid Sigal

专题命中 知识编辑与模型理解 ：prompting(title)；分类 cs.LG

Prompting Audios Using Acoustic Properties For Emotion Representation

Hira Dhamyal, Benjamin Elizalde, Soham Deshmukh, Huaming Wang, Bhiksha Raj, Rita Singh

专题命中 知识编辑与模型理解 ：prompting(title)；分类 cs.AI

Comments arXiv admin note: substantial text overlap with arXiv:2211.07737

Wave to Syntax: Probing spoken language models for syntax

Gaofei Shen, Afra Alishahi, Arianna Bisazza, Grzegorz Chrupała

专题命中 知识编辑与模型理解 ：language model(title)；分类 cs.CL

Comments Accepted to Interspeech 2023

Journal ref Proceedings of Interspeech 2023

VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts

Longtian Qiu, Renrui Zhang, Ziyu Guo, Ziyao Zeng, Zilu Guo, Yafeng Li, Guangnan Zhang

专题命中 知识编辑与模型理解 ：language model(title)；分类 cs.CL

Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining

Jian Guan, Feiyang Xiao, Youde Liu, Qiaoxi Zhu, Wenwu Wang

专题命中 知识编辑与模型理解 ：pretraining(title)；分类 cs.LG

Comments To appear in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)

Adapting Pre-trained Language Models for Quantum Natural Language Processing

Qiuchi Li, Benyou Wang, Yudong Zhu, Christina Lioma, Qun Liu

专题命中 知识编辑与模型理解 ：language model(title)；分类 cs.CL

Task Bias in Vision-Language Models

Sachit Menon, Ishaan Preetam Chandratreya, Carl Vondrick

专题命中 知识编辑与模型理解 ：language model(title)；分类 cs.LG

Comments First two authors contributed equally

Can Language Models Capture Graph Semantics? From Graphs to Language Model and Vice-Versa

Tarun Garg, Kaushik Roy, Amit Sheth

专题命中 知识编辑与模型理解 ：language model(title)；分类 cs.CL

Probing for Multilingual Numerical Understanding in Transformer-Based Language Models

Devin Johnson, Denise Mak, Drew Barker, Lexi Loessberg-Zahl

专题命中 知识编辑与模型理解 ：language model(title)；分类 cs.CL

Comments BlackboxNLP (EMNLP 2020)

Mechanistic Interpretability of Structure-Aware Numerical Reasoning in LLaMA 3.1 8B

LLaMA 3.1 8B中结构感知数值推理的机制可解释性

Rahul Chowdhury, Timothy A Rupprecht, Senhao Cao, Jiahao Liu, Octavia Camps, David Bau, Pu Zhao, Yanzhi Wang

机构 * Northeastern University（东北大学） ； EmbodyX Inc.（EmbodyX公司） 纠错

专题命中 知识编辑与模型理解 ：large language model(abstract)；language model(abstract)；分类 cs.AI、cs.LG

AI总结 本研究从机制可解释性视角探究LLaMA 3.1 8B，通过构建需捕捉结构的数值序列任务，发现其可无监督计算存储一阶差分，还揭示其通过类诱导回路机制完成数值推理。

Different Facets of Verbalised Overconfidence: an Interpretability Study

言语化过度自信的不同方面：一项可解释性研究

Davide Mazzaccara, Leonardo Bertolazzi, Raffaella Bernardi

机构 * CIMeC, University of Trento（特伦托大学认知科学与技术跨学科研究中心） ； DISI, University of Trento（特伦托大学信息工程与计算机科学系） ； Free University of Bozen-Bolzano（波尔扎诺自由大学） 纠错

专题命中 知识编辑与模型理解 ：large language model(abstract)；language model(abstract)；分类 cs.CL、cs.AI

AI总结 本研究以Qwen3-4B为对象，探究大型语言模型的过度自信现象，通过识别转码器特征揭示其默认机制偏向确定性生成，干预不确定性特征可缓解过度自信错误，且相关特征具有跨场景泛化性。

HalluTracer: Hallucination Detection via Depth-Averaging Truth Signals

HalluTracer：基于深度平均真值信号的幻觉检测方法

Zhihao Guo, Zonghan Wu, Huan Huo, DaYong Ye, Junwei Zhang, Weiran Yao, Zhiwei Liu, Qingsong Wen, Yilei Shao

机构 * University of Technology Sydney（悉尼科技大学） ； City University of Macau（澳门城市大学） ； Meta ； actAVA AI（actAVA人工智能公司） ； Microsoft AI（微软人工智能） ； Squirrel Ai Learning（松鼠人工智能学习公司） ； East China Normal University（华东师范大学） 纠错

专题命中 知识编辑与模型理解 ：large language model(abstract)；language model(abstract)；分类 cs.CL、cs.AI

AI总结 HalluTracer是一种在模型生成答案前聚合各层真值证据的幻觉检测框架，在六个开源语言模型和五个基准上优于白盒基线，将幻觉检测转化为深度聚合问题。

When Uncertainty Isn't Enough: An Empirical Study of Self-Correction in Code Generation

当不确定性还不够时：代码生成中自校正的实证研究

Pranav Rakasi, Maanas Lalwani, Arnav Srivastava, Arya Palanivel, Tinuade Adeleke, Ruizhe Li, Sean Wu

机构 * University of Michigan（密歇根大学） ； New York University（纽约大学） ； University of Wisconsin-Madison（威斯康星大学麦迪逊分校） ； Algoverse AI ； University of Aberdeen（阿伯丁大学） ； University of Oxford（牛津大学） 纠错

专题命中 知识编辑与模型理解 ：large language model(abstract)；language model(abstract)；分类 cs.AI、cs.LG

AI总结 本研究通过实证发现，针对代码生成的不确定性估计方法难以可靠提升生成准确率，仅基于验证的自校正策略可显著提升Pass@1指标，廉价不确定性估计器仅适合作为校正循环的门控信号。