Can LLMs Critique and Iterate on Their Own Outputs?
Read OriginalThis article analyzes the Reflexion concept from a recent arXiv preprint, where Large Language Models (LLMs) are used to evaluate and iteratively improve their own generated outputs. It discusses how this self-reflection mechanism can help correct hallucinations and inefficiencies, comparing capabilities across models like GPT-4, GPT-3.5, and Claude. The post connects the idea to broader AI research, including Constitutional AI, and provides a practical example using a non-rhyming poem prompt to demonstrate GPT-4's emergent self-critique ability.
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