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Attention Heads of Large Language Models: A Survey

Large language models (LLMs) have taken the world by storm, but their inner workings remain largely opaque. This lack of understanding is a significant hurdle for improving LLM performance. While most researchers have focused on enhancing LLMs through data-driven approaches, a growing number are diving deeper into their internal mechanisms to unlock their full potential.

A new survey paper, Attention Heads of Large Language Models: A Survey, delves into the world of attention heads, which are crucial components within LLMs responsible for processing information and making decisions. The authors first introduce a four-stage framework for LLM reasoning, mirroring the human thought process. They identify these stages as Knowledge Recalling, In-Context Identification, Latent Reasoning, and Expression Preparation. Using this framework, they categorize and analyze existing research on attention heads, identifying various types of heads specialized for different tasks.

The paper highlights concrete examples of attention heads with specialized functions. For instance, the “Memory Head” retrieves relevant knowledge from the model’s internal store, similar to how humans recall information. The “Constant Head” distributes attention evenly across all options when tackling multiple-choice questions, acting as a baseline for comparison. The “Single Letter Head” focuses attention on individual letters to identify potential answers, mimicking the meticulousness of human examination.

Furthermore, the paper explores various methodologies used to discover and analyze attention heads. They classify these methods as “Modeling-Free” and “Modeling-Required,” depending on whether the research requires building new models. “Modeling-Free” methods, like Activation Patching and Ablation Study, modify the model’s latent state without constructing new models, while “Modeling-Required” methods involve training new models or using simplified versions of LLMs.

The authors provide a detailed overview of commonly used evaluation benchmarks and datasets for LLM interpretability research, categorizing them as Mechanism Exploration Evaluation and Common Evaluation. The former focuses on specific attention heads while the latter assesses the overall performance of LLMs after modifications.

The paper concludes by discussing limitations in current research, emphasizing the need for more comprehensive frameworks that analyze the collaboration between multiple attention heads. The authors also highlight the importance of developing new experimental methods and integrating Machine Psychology to understand the connection between human cognition and LLMs.

This survey paper presents a comprehensive overview of the latest research on attention heads in LLMs. It provides a valuable resource for researchers seeking to understand the internal workings of LLMs and develop new techniques for enhancing their performance. By illuminating the intricate world of attention heads, this paper paves the way for a more transparent and efficient future for LLMs.