Attention Heads of Large Language Models: A Survey
<|reference_start|>Attention Heads of Large Language Models: A Survey: Since the advent of ChatGPT, Large Language Models (LLMs) have excelled in various tasks but remain as black-box systems. Consequently, the reasoning bottlenecks of LLMs are mainly influenced by their internal architecture. As a result, many researchers have begun exploring the potential internal mechanisms of LLMs, with most studies focusing on attention heads. Our survey aims to shed light on the internal reasoning processes of LLMs by concentrating on the underlying mechanisms of attention heads. We first distill the human thought process into a four-stage framework: Knowledge Recalling, In-Context Identification, Latent Reasoning, and Expression Preparation. Using this framework, we systematically review existing research to identify and categorize the functions of specific attention heads. Furthermore, we summarize the experimental methodologies used to discover these special heads, dividing them into two categories: Modeling-Free methods and Modeling-Required methods. Also, we outline relevant evaluation methods and benchmarks. Finally, we discuss the limitations of current research and propose several potential future directions.<|reference_end|>
@article{zheng2024attention,
    title={Attention Heads of Large Language Models: A Survey},
    author={Zifan Zheng, Yezhaohui Wang, Yuxin Huang, Shichao Song, Mingchuan
  Yang, Bo Tang, Feiyu Xiong, Zhiyu Li},
    journal={arXiv preprint arXiv:2409.03752},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03752},
    primaryClass={cs.CL}
}