Glitch Tokens in Large Language Models: Categorization Taxonomy and Effective Detection
With the expanding application of Large Language Models (LLMs) in various domains, it becomes imperative to comprehensively investigate their unforeseen behaviors and consequent outcomes. In this study, we introduce and systematically explore the phenomenon of “glitch tokens”, which are anomalous tokens produced by established tokenizers and could potentially compromise the models’ quality of response. Specifically, we experiment on seven top popular LLMs utilizing three distinct tokenizers and involving a totally of 182,517 tokens. We present categorizations of the identified glitch tokens and symptoms exhibited by LLMs when interacting with glitch tokens. Based on our observation that glitch tokens tend to cluster in the embedding space, we propose GlitchHunter, a novel iterative clustering-based technique, for efficient glitch token detection. The evaluation shows that our approach notably outperforms three baseline methods on eight open-source LLMs. To the best of our knowledge, we present the first comprehensive study on glitch tokens. Our new detection further provides valuable insights into mitigating tokenization-related errors in LLMs.
Wed 17 JulDisplayed time zone: Brasilia, Distrito Federal, Brazil change
14:00 - 15:30 | AI4SE 1Research Papers at Pitomba Chair(s): Mauro Pezze USI Università della Svizzera Italiana & SIT Schaffhausen Institute of Technology | ||
14:00 18mTalk | Are Human Rules Necessary? Generating Reusable APIs with CoT Reasoning and In-context Learning Research Papers Yubo Mai Zhejiang University, Zhipeng Gao Shanghai Institute for Advanced Study - Zhejiang University, Xing Hu Zhejiang University, Lingfeng Bao Zhejiang University, Yu Liu Zhejiang University, JianLing Sun Zhejiang University | ||
14:18 18mTalk | CodeArt: Better Code Models by Attention Regularization When Symbols Are Lacking Research Papers Zian Su Purdue University, Xiangzhe Xu Purdue University, Ziyang Huang Purdue University, Zhuo Zhang Purdue University, Yapeng Ye Purdue University, Jianjun Huang Renmin University of China, Xiangyu Zhang Purdue University | ||
14:36 18mTalk | Enhancing Code Understanding for Impact Analysis by Combining Transformers and Program Dependence Graphs Research Papers Yanfu Yan William & Mary, Nathan Cooper William & Mary, Kevin Moran University of Central Florida, Gabriele Bavota Software Institute @ Università della Svizzera Italiana, Denys Poshyvanyk William & Mary, Steve Rich Cisco Systems | ||
14:54 18mTalk | Exploring and Unleashing the Power of Large Language Models in Automated Code Translation Research Papers Zhen Yang Shandong University, Fang Liu Beihang University, Zhongxing Yu Shandong University, Jacky Keung City University of Hong Kong, Jia Li Peking University, Shuo Liu City University of Hong Kong, Hong Yifan City University of Hong Kong, Xiaoxue Ma City University of Hong Kong, Zhi Jin Peking University, Ge Li Peking University Pre-print | ||
15:12 18mTalk | Glitch Tokens in Large Language Models: Categorization Taxonomy and Effective Detection Research Papers Yuxi Li Huazhong University of Science and Technology, Yi Liu Nanyang Technological University, Gelei Deng Nanyang Technological University, Ying Zhang Virginia Tech, Wenjia Song Virginia Tech, Ling Shi Nanyang Technological University, Kailong Wang Huazhong University of Science and Technology, Yuekang Li The University of New South Wales, Yang Liu Nanyang Technological University, Haoyu Wang Huazhong University of Science and Technology | ||