RAMIE, a retrieval-augmented multi-task instruction-tuned framework, improves LLM information extraction for dietary supplements from clinical records, with RAG recovering accuracy lost in multi-task training.
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.
fields
cs.CL 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
RAMIE: Retrieval-Augmented Multi-task Information Extraction with Large Language Models on Dietary Supplements
RAMIE, a retrieval-augmented multi-task instruction-tuned framework, improves LLM information extraction for dietary supplements from clinical records, with RAG recovering accuracy lost in multi-task training.