Self-Reflection in LLM Agents: Effects on Problem-Solving Performance
<|reference_start|>Self-Reflection in LLM Agents: Effects on Problem-Solving Performance: In this study, we investigated the effects of self-reflection in large language models (LLMs) on problem-solving performance. We instructed nine popular LLMs to answer a series of multiple-choice questions to provide a performance baseline. For each incorrectly answered question, we instructed eight types of self-reflecting LLM agents to reflect on their mistakes and provide themselves with guidance to improve problem-solving. Then, using this guidance, each self-reflecting agent attempted to re-answer the same questions. Our results indicate that LLM agents are able to significantly improve their problem-solving performance through self-reflection ($p < 0.001$). In addition, we compared the various types of self-reflection to determine their individual contribution to performance. All code and data are available on GitHub at https://github.com/matthewrenze/self-reflection<|reference_end|>
@article{renze2024self-reflection,
    title={Self-Reflection in LLM Agents: Effects on Problem-Solving Performance},
    author={Matthew Renze, Erhan Guven},
    journal={arXiv preprint arXiv:2405.06682},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06682},
    primaryClass={cs.CL cs.AI}
}