基於自生成範例的零樣本命名實體識別框架：ReverseNER
ReverseNER 透過反轉傳統 NER 流程，利用大型語言模型 (LLM) 生成高品質、與任務相關的範例庫，從而提高零樣本命名實體識別 (NER) 的效能。
대형 언어 모델을 사용한 제로샷 개체명 인식을 위한 자체 생성 예제 기반 프레임워크, ReverseNER
ReverseNER는 기존 NER 방식을 뒤집어 개체명을 먼저 생성하고 이를 활용해 문장을 생성하는 방식으로 예제 라이브러리를 구축하여, 라벨링된 데이터 없이도 대형 언어 모델이 제로샷 개체명 인식 작업을 효과적으로 수행하도록 돕는 프레임워크이다.
ReverseNER: Using Large Language Models and Self-Generated Examples for Zero-Shot Named Entity Recognition
ReverseNER is a novel approach that enhances zero-shot Named Entity Recognition (NER) by using Large Language Models (LLMs) to generate and leverage example sentences containing pre-defined entity types, improving performance in scenarios with limited labeled data.
Zero-Shot Named Entity Recognition on Italian Language
SLIMER-IT, an instruction-tuning approach for zero-shot named entity recognition, leverages prompts enriched with definitions and guidelines to outperform state-of-the-art models on unseen entity types in Italian.
Enriching Prompts with Definitions and Guidelines for Improved Zero-Shot Named Entity Recognition
Providing definition and guidelines in the prompt can improve the performance and robustness of instruction-tuned language models for zero-shot named entity recognition, especially on unseen entity types.
Self-Improving Framework for Zero-Shot Named Entity Recognition with Large Language Models
Proposing a training-free self-improving framework for zero-shot NER with LLMs significantly improves performance.
© 2024 by Linnk AI