Year: 2026
Type: conference-paper
Abstract: The capabilities of Large Language Models (LLMs) are limited to some extent by pretraining, 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.
Authors: Haoran Sun, Zekun Zhang, Shaoning Zeng
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G6163288384, G6885115677
OpenAlex ID: W7166884525