Empirical Study of Zero-Shot NER with C hat GPT (2023)

Abstract: Large language models (LLMs) exhibited powerful capability in various natural language processing tasks. This work focuses on exploring LLM performance on zero-shot information extraction, with a focus on the ChatGPT and named entity recognition (NER) task. Inspired by the remarkable reasoning capability of LLM on symbolic and arithmetic reasoning, we adapt the prevalent reasoning methods to NER and propose reasoning strategies tailored for NER. First, we explore a decomposed question-answering paradigm by breaking down the NER task into simpler subproblems by labels. Second, we propose syntactic augmentation to stimulate the model’s intermediate thinking in two ways: syntactic prompting, which encourages the model to analyze the syntactic structure itself, and tool augmentation, which provides the model with the syntactic information generated by a parsing tool. Besides, we adapt self-consistency to NER by proposing a two-stage majority voting strategy, which first votes for the most consistent mentions, then the most consistent types. The proposed methods achieve remarkable improvements for zero-shot NER across seven benchmarks, including Chinese and English datasets, and on both domain-specific and general-domain scenarios. In addition, we present a comprehensive analysis of the error types with suggestions for optimization directions. We also verify the effectiveness of the proposed methods on the few-shot setting and other LLMs.

## introduction large language models (llms) @xcite @xcite have brought revolutions in natural language processing (nlp) due to the remarkable zero-shot and few-shot generalization. one of the most wellknown llms, chatgpt (openai, 2022) powered by gpt3.5 and gpt4 @xcite , has exhibited strong dialogue capabilities. as a closed model, chatgpt sparked a lot of work for its evaluation and application on diverse tasks and aspects @xcite @xcite . information extraction (ie) is a fundamental topic in nlp, which aims to extract structured information from unstructured text, including tasks such as named entity recognition (ner) @xcite , relation extraction (re) @xcite , event extraction (ee) @xcite , etc. evaluating chatgpt's performance on ie is important for understanding its capabilities in structured prediction and language understanding @xcite . with recent techniques for eliciting complex multi-step reasoning @xcite , llms have shown remarkable zero-shot reasoning ability in arithmetic and symbolic reasoning @xcite . however, the reasoning ability of llm on ie remained unexplored. to mitigate this gap, we present a systematic empirical study exploring the reasoning capability of llm on ie, with a focus on the chatgpt and zero-shot ner task. by adapting the prevalent reasoning techniques @xcite @xcite to ner, we propose the following strategies to stimulate the reasoning potential of llm on ner: the main contributions of this paper include: ## method adapting the prevalent reasoning techniques @xcite @xcite to ner, we propose four strategies to stimulate the reasoning capabilities of llm on ner. examples of the proposed methods are shown in fig. 1 . ## conclusion we present an empirical study of zero-shot ner with chatgpt, with four proposed strategies to simulate the reasoning potential of chatgpt on ner. inspired by the powerful reasoning capabilities of llm on logical and arithmetic reasoning tasks, the proposed strategies involve task decomposition, syntactic augmentation, and tailored sc. we verify the effectiveness of our methods on chinese and english scenarios, and on both domain-specific and general-domain datasets. we provide an analysis of the error types with suggested solutions. besides, we verify the effectiveness of the proposed methods on the few-shot setting and other llms.