Pipeline Chain-of-Thought: A Prompt Method for Large Language Model Relation Extraction
Author:
Affiliation:
1. School of Computer Science and Technology, XinJiang University,Xinjiang Key Laboratory of Signal Detection and Processing,Urumqi,China
Funder
Natural Science Foundation - Surface
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10336886/10336974/10337264.pdf?arnumber=10337264
Reference28 articles.
1. Evaluating ChatGPTs Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness;Li;arXiv,2023
2. Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
3. Ace 2005 multilingual training corpus;Walker;Prog. Theor. Phys. SuppI.,2006
4. Prompt-Learning for Fine-Grained Entity Typing;Ding;arXiv,2021
5. How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?
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1. The method of utilizing prompt engineering to train large language models for the intelligent expression of affair-elements;Procedia Computer Science;2024
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