语言大模型 / LLM

Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models

Yuyan Chen, Qiang Fu, Yichen Yuan, Zhihao Wen, Ge Fan, Dayiheng Liu, Dongmei Zhang, Zhixu Li, Yanghua Xiao

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

Comments Accepted to CIKM 2023 (Long Paper)

Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models

Alfonso Amayuelas, Kyle Wong, Liangming Pan, Wenhu Chen, William Wang

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

PFME: A Modular Approach for Fine-grained Hallucination Detection and Editing of Large Language Models

Kunquan Deng, Zeyu Huang, Chen Li, Chenghua Lin, Min Gao, Wenge Rong

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

Beyond Under-Alignment: Atomic Preference Enhanced Factuality Tuning for Large Language Models

Hongbang Yuan, Yubo Chen, Pengfei Cao, Zhuoran Jin, Kang Liu, Jun Zhao

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

Can Large Language Models Follow Concept Annotation Guidelines? A Case Study on Scientific and Financial Domains

Marcio Fonseca, Shay B. Cohen

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

Comments ACL 2024 camera ready

The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models

Shuo Zhang, Liangming Pan, Junzhou Zhao, William Yang Wang

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

Comments ACL 2024, Findings

Cross-Lingual Knowledge Editing in Large Language Models

Jiaan Wang, Yunlong Liang, Zengkui Sun, Yuxuan Cao, Jiarong Xu, Fandong Meng

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

Comments Accepted to ACL 2024 main conference

Active Use of Latent Constituency Representation in both Humans and Large Language Models

Wei Liu, Ming Xiang, Nai Ding

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

Comments 62 pages, 5 figures. Under review

Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval

Mengjia Niu, Hao Li, Jie Shi, Hamed Haddadi, Fan Mo

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

Can Large Language Models put 2 and 2 together? Probing for Entailed Arithmetical Relationships

D. Panas, S. Seth, V. Belle

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

AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts

Meng Jiang, Yi Jing Yu, Qing Zhao, Jianqiang Li, Changwei Song, Hongzhi Qi, Wei Zhai, Dan Luo, Xiaoqin Wang, Guanghui Fu, Bing Xiang Yang

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

"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models

Seth Bernstein, Paul Denny, Juho Leinonen, Lauren Kan, Arto Hellas, Matt Littlefield, Sami Sarsa, Stephen MacNeil

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

Comments 7 pages, 2 figures, ITiCSE 2024 preprint

Using Large Language Models to Enhance Programming Error Messages

Juho Leinonen, Arto Hellas, Sami Sarsa, Brent Reeves, Paul Denny, James Prather, Brett A. Becker

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

Comments 7 pages, accepted for publication at SIGCSE TS 2023

A Question on the Explainability of Large Language Models and the Word-Level Univariate First-Order Plausibility Assumption

Jeremie Bogaert, Francois-Xavier Standaert

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

Comments 7 pages, 10 figures, Accepted and presented at AAAI 2024 (ReLM workshop)

On the Origins of Linear Representations in Large Language Models

Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam, Victor Veitch

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

From Understanding to Utilization: A Survey on Explainability for Large Language Models

Haoyan Luo, Lucia Specia

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

Framework-Based Qualitative Analysis of Free Responses of Large Language Models: Algorithmic Fidelity

Aliya Amirova, Theodora Fteropoulli, Nafiso Ahmed, Martin R. Cowie, Joel Z. Leibo

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

Comments 52 pages, 5 tables, 5 figures

ProAgent: Building Proactive Cooperative Agents with Large Language Models

Ceyao Zhang, Kaijie Yang, Siyi Hu, Zihao Wang, Guanghe Li, Yihang Sun, Cheng Zhang, Zhaowei Zhang, Anji Liu, Song-Chun Zhu, Xiaojun Chang, Junge Zhang, Feng Yin, Yitao Liang, Yaodong Yang

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

Comments v3 is the AAAI'24 camera ready version, which polished abstract and introduction based on the reviewers' comments, and enriched related works. 7 pages of main content, 2 pages of references, 2 figures and 1 table

Understanding Your Agent: Leveraging Large Language Models for Behavior Explanation

Xijia Zhang, Yue Guo, Simon Stepputtis, Katia Sycara, Joseph Campbell

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

Minimizing Factual Inconsistency and Hallucination in Large Language Models

Muneeswaran I, Shreya Saxena, Siva Prasad, M V Sai Prakash, Advaith Shankar, Varun V, Vishal Vaddina, Saisubramaniam Gopalakrishnan

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

Active Learning Principles for In-Context Learning with Large Language Models

Katerina Margatina, Timo Schick, Nikolaos Aletras, Jane Dwivedi-Yu

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

Comments To appear at Findings of EMNLP (Camera Ready version)

Probing the Creativity of Large Language Models: Can models produce divergent semantic association?

Honghua Chen, Nai Ding

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

Comments Accepted for publication in Findings of EMNLP 2023

Towards Mitigating Hallucination in Large Language Models via Self-Reflection

Ziwei Ji, Tiezheng Yu, Yan Xu, Nayeon Lee, Etsuko Ishii, Pascale Fung

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

Comments Accepted by the findings of EMNLP 2023

Chain-of-Verification Reduces Hallucination in Large Language Models

Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, Jason Weston

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

Large language models converge toward human-like concept organization

Mathias Lykke Gammelgaard, Jonathan Gabel Christiansen, Anders Søgaard

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

Large Language Models and Knowledge Graphs: Opportunities and Challenges

Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira Vakaj, Mauro Dragoni, Damien Graux

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

Comments 30 pages

Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking

Qinyong Wang, Zhenxiang Gao, Rong Xu

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

Can Large Language Models Change User Preference Adversarially?

Varshini Subhash

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

Model-Agnostic Policy Explanations with Large Language Models

Zhang Xi-Jia, Yue Guo, Shufei Chen, Simon Stepputtis, Matthew Gombolay, Katia Sycara, Joseph Campbell

机构 * Georgia Institute of Technology（佐治亚理工学院） ； Carnegie Mellon University（卡内基梅隆大学） ； Purdue University（普渡大学） 纠错

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

Comments To appear in Proceedings of the 2nd Conference on Language Modeling (COLM 2025). arXiv admin note: substantial text overlap with arXiv:2311.18062

HINT: Toward an Executable Hardware-Intent Representation Layer for LLM-Driven RTL Generation

HINT：面向大语言模型驱动的RTL生成的可执行硬件意图表示层

Tairan Cheng, Yi Liu, Dongsheng Zuo, Zhengyuan Shi, Hongji Zhang, Xiangfei Hu, Maoshuo He, Hao Yan, Qiang Xu

专题命中 知识编辑与模型理解 ：LLM(title,summary_cn)；large language model(abstract)；language model(abstract)

AI总结 本文提出HINT可执行硬件意图表示层，用于LLM驱动的RTL生成，在7个算子案例中全量生成合规可综合RTL，面积较人工实现及直接C2RTL结果显著降低，还验证了其在各类复杂设计中的有效性。