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今天的主题是：A Theoretical Understanding of Self-Correction through In-context Alignment

Summary

This research paper examines the ability of large language models (LLMs) to self-correct, specifically focusing on how this capability arises from an in-context alignment perspective. The authors present a theoretical analysis demonstrating that standard transformer architectures can perform gradient descent on common alignment objectives in an in-context manner, highlighting the crucial roles played by softmax attention, feed-forward networks, and stacked layers. They explore the practical application of intrinsic self-correction in real-world scenarios, showcasing its efficacy in alleviating social biases and defending against jailbreak attacks. The paper provides concrete theoretical and empirical insights into the potential for building LLMs that can autonomously improve their performance through self-correction.

原文链接：https://openreview.net/pdf?id=OtvNLTWYww

解读链接：https://www.jiqizhixin.com/articles/2024-11-18-3