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About this paper
Jinglong Gao, Xiao Ding, Yiming Cui, Jianbai Zhao, Hepeng Wang, Ting Liu, Bing Qin
Self-Evolving GPT (SE-GPT), developed by researchers from Harbin Institute of Technology and iFLYTEK Research, enables large language models to autonomously learn and improve over time by accumulating and applying task-solving experience. The framework enhanced GPT-3.5's performance by 3.8% and GPT-4's by 5.3% on average across six diverse NLP benchmarks.
Key insights
- Efficient experience transfer across similar tasks allows for effective knowledge sharing and initial strategy formulation.
- Autonomous practice, enhanced by web information retrieval, enables the LLM to generate diverse examples, self-solve, and evaluate, leading to robust experience induction.
- The cumulative growth of both distinct tasks and granular procedural and suggestive experiences over time contributes to continuous and adaptive learning.
Summary by alphaXiv.
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