Reproduce “OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Yujie Luo, Xiangyuan Ru, Kangwei Liu, Lin Yuan, Mengshu Sun, Ningyu Zhang, Lei Liang, Zhiqiang Zhang and 5 more
Researchers from Zhejiang University and Ant Group developed OneKE, a Dockerized, schema-guided multi-agent system that enhances knowledge extraction using Large Language Models. It achieves improved accuracy and robustness by orchestrating specialized agents, dynamic schema generation, and an iterative error correction mechanism based on past extraction cases.
Key insights
- Providing LLMs with relevant few-shot examples (Case Retrieval) significantly enhances extraction accuracy, especially for complex tasks like Relation Extraction, by guiding the model with successful reasoning steps.
- Integrating an error debugging and correction mechanism (Case Reflection) allows the system to continuously improve by learning from past mistakes ("Bad Cases"), leading to more robust and reliable outputs.
- A modular multi-agent design with a dynamic knowledge base enables a flexible and adaptable knowledge extraction system that can handle diverse schemas and data sources without explicit model retraining.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.