Abstract
Few-shot relation extraction involves identifying the type of relationship between two specific entities within a text, using a limited number of annotated samples. A variety of solutions to this problem have emerged by applying meta-learning and neural graph techniques which typically necessitate a training process for adaptation. Recently, the strategy of in-context learning has been demonstrating notable results without the need of training. Few studies have already utilized in-context learning for zero-shot information extraction. Unfortunately, the evidence for inference is either not considered or implicitly modeled during the construction of chain-of-thought prompts. In this paper, we propose a novel approach for few-shot relation extraction using large language models, named CoT-ER, chain-of-thought with explicit evidence reasoning. In particular, CoT-ER first induces large language models to generate evidences using task-specific and concept-level knowledge. Then these evidences are explicitly incorporated into chain-of-thought prompting for relation extraction. Experimental results demonstrate that our CoT-ER approach (with 0% training data) achieves competitive performance compared to the fully-supervised (with 100% training data) state-of-the-art approach on the FewRel1.0 and FewRel2.0 datasets.- Anthology ID:
- 2023.findings-emnlp.153
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2334–2352
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.153
- DOI:
- 10.18653/v1/2023.findings-emnlp.153
- Cite (ACL):
- Xilai Ma, Jing Li, and Min Zhang. 2023. Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2334–2352, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction (Ma et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-emnlp.153.pdf