Reproduce “Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction” 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
Guozheng Li, Peng Wang, Wenjun Ke, Yikai Guo, Ke Ji, Ziyu Shang, Jiajun Liu, Zijie Xu
A framework named RE^4 (Recall, Retrieve, Reason) enhances in-context relation extraction by equipping moderate-sized open-source large language models with ontological knowledge for relevant demonstration retrieval and robust in-context reasoning. The method achieves state-of-the-art or competitive F1 scores across multiple benchmarks, surpassing many prior supervised and ICL approaches.
Key insights
- Distilling ontological knowledge into LLMs allows them to generate highly relevant entity pairs, significantly improving the quality and specificity of retrieved demonstrations for RE.
- Dedicated instruction tuning is crucial for boosting the in-context learning capabilities of moderate-sized open-source LLMs, enabling them to perform complex relation extraction tasks competitively.
- A synergistic framework that combines targeted retrieval based on ontological consistency with enhanced in-context reasoning can overcome limitations of prior ICL methods for RE.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.