Problem
Research questions and friction points this paper is trying to address.
enterprise information extraction
personalized prompts
user heterogeneity
One Prompt Does Not Fit All: Self-Meta-Evolve for Personalized Information Extraction
🤖 AI Summary
为解决企业信息提取中用户需求多样性问题，提出Self-Meta-Evolve框架，通过个性化提示适应与反馈优化方法，提高信息提取成功率。
📝 Abstract
Large language models (LLMs) are increasingly deployed for enterprise information extraction (IE), where the same document must be reorganized differently for each user. Existing prompt optimization methods, however, rely on a single prompt optimized against a global objective, which is misaligned with the inherent user heterogeneity of real workplaces. We formulate enterprise IE as per-user prompt adaptation under interaction feedback and propose Self-Meta-Evolve, a hierarchical framework that maintains a dedicated prompt for each user and continuously refines it through a dual-loop process: an inner loop that edits structured prompts based on persona-conditioned feedback, and an outer loop that evolves the meta-prompt itself by distilling successful editing patterns. To enable scalable training and evaluation, we release a persona-driven IE benchmark of 292 simulated enterprise users, paired with a reproducible persona-generation pipeline grounded in O*NET occupational taxonomies. On this benchmark, Self-Meta-Evolve achieves a 74.58% success rate, outperforming the strongest prompt-optimization baseline by 13.56 absolute points, and reaches 52.54\% within only two iterations. A double-blind human study with twenty real professionals further confirms that prompts adapted by our framework win against static baselines in 71% of pairwise comparisons.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Self-Meta-Evolve
personalized information extraction
dual-loop process
persona-conditioned feedback
meta-prompt evolution
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