Computer Science > Computation and Language
[Submitted on 1 Oct 2023 (v1), last revised 1 Feb 2024 (this version, v4)]
Title:SELF: Self-Evolution with Language Feedback
View PDF HTML (experimental)Abstract:Large Language Models (LLMs) have demonstrated remarkable versatility across various domains. To further advance LLMs, we propose 'SELF' (Self-Evolution with Language Feedback), a novel approach that enables LLMs to self-improve through self-reflection, akin to human learning processes. SELF initiates with a meta-skill learning process that equips the LLMs with capabilities for self-feedback and self-refinement. Subsequently, the model undergoes an iterative process of self-evolution. In each iteration, it utilizes an unlabeled dataset of instructions to generate initial responses. These responses are enhanced through self-feedback and self-refinement. The model is then fine-tuned using this enhanced data. The model undergoes progressive improvement through this iterative self-evolution process. Moreover, the SELF framework enables the model to apply self-refinement during inference, which further improves response quality. Our experiments in mathematics and general tasks demonstrate that SELF can enhance the capabilities of LLMs without human intervention. The SELF framework indicates a promising direction for the autonomous evolution of LLMs, transitioning them from passive information receivers to active participants in their development.
Submission history
From: Jianqiao Lu [view email][v1] Sun, 1 Oct 2023 00:52:24 UTC (354 KB)
[v2] Sat, 7 Oct 2023 09:57:58 UTC (354 KB)
[v3] Thu, 30 Nov 2023 02:18:10 UTC (375 KB)
[v4] Thu, 1 Feb 2024 06:10:00 UTC (1,020 KB)
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