A Novel Self-Evolution Framework for Large Language Models

• URL: http://arxiv.org/abs/2507.15281v1
• Date: Mon, 21 Jul 2025 06:30:39 GMT
• Title: A Novel Self-Evolution Framework for Large Language Models
• Authors: Haoran Sun, Zekun Zhang, Shaoning Zeng,
• Abstract summary: We propose a novel Dual-Phase Self-Evolution framework to jointly optimize user preference adaptation and domain-specific competence.<n>Experiments across general NLP benchmarks and long-term dialogue tasks demonstrate that DPSE consistently outperforms Supervised Fine-Tuning, Preference Optimization, and Memory-Augmented baselines.
• Score: 18.62332474172811
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The capabilities of Large Language Models (LLMs) are limited to some extent by pre-training, so some researchers optimize LLMs through post-training. Existing post-training strategies, such as memory-based retrieval or preference optimization, improve user alignment yet fail to enhance the model's domain cognition. To bridge this gap, we propose a novel Dual-Phase Self-Evolution (DPSE) framework that jointly optimizes user preference adaptation and domain-specific competence. DPSE introduces a Censor module to extract multi-dimensional interaction signals and estimate satisfaction scores, which guide structured data expansion via topic-aware and preference-driven strategies. These expanded datasets support a two-stage fine-tuning pipeline: supervised domain grounding followed by frequency-aware preference optimization. Experiments across general NLP benchmarks and long-term dialogue tasks demonstrate that DPSE consistently outperforms Supervised Fine-Tuning, Preference Optimization, and Memory-Augmented baselines. Ablation studies validate the contribution of each module. In this way, our framework provides an autonomous path toward continual self-evolution of LLMs.

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