解析大模型决策机制，助力对齐优化与可解释性提升
Mechanistic Interpretability for Large Language Model Alignment: Progress, Challenges, and Future Directions
- 通过电路发现、激活操控等方法解析模型内部计算结构
- 揭示神经元超叠加与多义性带来的理解难题
- 适合关注大模型对齐与可解释性研究的学者参考
大型语言模型在多种任务中表现出色，但其内部决策过程仍不透明。机械可解释性（即通过学习表征与计算结构系统研究神经网络如何实现算法）已成为理解并对齐这些模型的关键方向。本文综述了近期应用于大模型对齐的机械可解释性技术进展，涵盖电路发现、特征可视化、激活操控和因果干预等方法。分析表明，可解释性洞察已推动强化学习人类反馈（RLHF）、宪法式AI及可扩展监督等对齐策略的发展。主要挑战包括超叠加假说、神经元多义性以及大规模模型涌现行为的解释困难。未来研究应聚焦自动化可解释性、跨模型电路泛化，以及可扩展至前沿模型的可解释性驱动对齐技术。
原文摘要 · Abstract (English)
Large language models (LLMs) have achieved remarkable capabilities across diverse tasks, yet their internal decision-making processes remain largely opaque. Mechanistic interpretability (i.e., the systematic study of how neural networks implement algorithms through their learned representations and computational structures) has emerged as a critical research direction for understanding and aligning these models. This paper surveys recent progress in mechanistic interpretability techniques applied to LLM alignment, examining methods ranging from circuit discovery to feature visualization, activation steering, and causal intervention. We analyze how interpretability insights have informed alignment strategies including reinforcement learning from human feedback (RLHF), constitutional AI, and scalable oversight. Key challenges are identified, including the superposition hypothesis, polysemanticity of neurons, and the difficulty of interpreting emergent behaviors in large-scale models. We propose future research directions focusing on automated interpretability, cross-model generalization of circuits, and the development of interpretability-driven alignment techniques that can scale to frontier models.
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