WINELL: Wikipedia Never-Ending Updating with LLM Agents
This addresses the challenge of manual updates for Wikipedia editors, offering an incremental improvement in automating knowledge base maintenance.
The paper tackles the problem of keeping Wikipedia content up-to-date by introducing WiNELL, an LLM-based agentic framework that automatically identifies and suggests factual updates, outperforming baselines like GPT-4o in information coverage and editing efficiency.
Wikipedia, a vast and continuously consulted knowledge base, faces significant challenges in maintaining up-to-date content due to its reliance on manual human editors. Inspired by the vision of continuous knowledge acquisition in NELL and fueled by advances in LLM-based agents, this paper introduces WiNELL, an agentic framework for continuously updating Wikipedia articles. Our approach employs a multi-agent framework to aggregate online information, select new and important knowledge for a target entity in Wikipedia, and then generate precise edit suggestions for human review. Our fine-grained editing models, trained on Wikipedia's extensive history of human edits, enable incorporating updates in a manner consistent with human editing behavior. Our editor models outperform both open-source instruction-following baselines and closed-source LLMs (e.g., GPT-4o) in key information coverage and editing efficiency. End-to-end evaluation on high-activity Wikipedia pages demonstrates WiNELL's ability to identify and suggest timely factual updates. This opens up a promising research direction in LLM agents for automatically updating knowledge bases in a never-ending fashion.