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The rapid development of large language models (LLMs) has not only providednumerous opportunities but also presented significant challenges. This becomesparticularly evident when LLMs inadvertently generate harmful or toxic content,either unintentionally or because of intentional inducement. Existing alignmentmethods usually direct LLMs toward the favorable outcomes by utilizinghuman-annotated, flawless instruction-response pairs. Conversely, this studyproposes a novel alignment technique based on mistake analysis, whichdeliberately exposes LLMs to erroneous content to learn the reasons formistakes and how to avoid them. In this case, mistakes are repurposed intovaluable data for alignment, effectively helping to avoid the production oferroneous responses. Without external models or human annotations, our methodleverages a model's intrinsic ability to discern undesirable mistakes andimproves the safety of its generated responses. Experimental results revealthat our method outperforms existing alignment approaches in enhancing modelsafety while maintaining the overall utility.

Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis