{"slug": "a-dual-phase-self-evolution-framework-for-large-language-models", "title": "A Dual-Phase Self-Evolution Framework for Large Language Models", "summary": "Researchers at the Association for Computational Linguistics introduced a Dual-Phase Self-Evolution (DPSE) framework for large language models that jointly optimizes user preference adaptation and domain-specific competence. The framework uses a Censor module to extract interaction signals and guide data expansion, outperforming existing methods on general NLP benchmarks and long-term dialogue tasks. This work provides an autonomous path for continual self-evolution of LLMs.", "body_md": "##### Abstract\n\nThe 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.- Anthology ID:\n- 2026.findings-acl.37\n- Volume:\n[Findings of the Association for Computational Linguistics: ACL 2026](/volumes/2026.findings-acl/)- Month:\n- July\n- Year:\n- 2026\n- Address:\n- San Diego, California, United States\n- Editors:\n[Maria Liakata](/people/maria-liakata/),[Viviane P. Moreira](/people/viviane-p-moreira/unverified/),[Jiajun Zhang](/people/jiajun-zhang/unverified/),[David Jurgens](/people/david-jurgens/)- Venue:\n[Findings](/venues/findings/)- SIG:\n- Publisher:\n- Association for Computational Linguistics\n- Note:\n- Pages:\n- 772–782\n- Language:\n- URL:\n[https://aclanthology.org/2026.findings-acl.37/](https://aclanthology.org/2026.findings-acl.37/)- DOI:\n- Cite (ACL):\n- Haoran Sun, Zekun Zhang, and Shaoning Zeng. 2026.\n[A Dual-Phase Self-Evolution Framework for Large Language Models](https://aclanthology.org/2026.findings-acl.37/). In*Findings of the Association for Computational Linguistics: ACL 2026*, pages 772–782, San Diego, California, United States. Association for Computational Linguistics. - Cite (Informal):\n[A Dual-Phase Self-Evolution Framework for Large Language Models](https://aclanthology.org/2026.findings-acl.37/)(Sun et al., Findings 2026)- PDF:\n[https://aclanthology.org/2026.findings-acl.37.pdf](https://aclanthology.org/2026.findings-acl.37.pdf)", "url": "https://wpnews.pro/news/a-dual-phase-self-evolution-framework-for-large-language-models", "canonical_source": "https://aclanthology.org/2026.findings-acl.37/", "published_at": "2026-06-22 00:00:00+00:00", "updated_at": "2026-06-26 08:17:30.780235+00:00", "lang": "en", "topics": ["large-language-models", "natural-language-processing", "ai-research"], "entities": ["Association for Computational Linguistics", "ACL", "Haoran Sun", "Zekun Zhang", "Shaoning Zeng"], "alternates": {"html": "https://wpnews.pro/news/a-dual-phase-self-evolution-framework-for-large-language-models", "markdown": "https://wpnews.pro/news/a-dual-phase-self-evolution-framework-for-large-language-models.md", "text": "https://wpnews.pro/news/a-dual-phase-self-evolution-framework-for-large-language-models.txt", "jsonld": "https://wpnews.pro/news/a-dual-phase-self-evolution-framework-for-large-language-models.jsonld"}}