重新审视大型语言模型作为零样本关系抽取器
Revisiting Large Language Models as Zero-shot Relation Extractors
AI总结：
本文研究ChatGPT等LLMs作为零样本关系抽取器，提出SumAsk提示将RE转为QA格式，实验表明其显著提升性能且优于全监督方法，在重叠和NoTA关系上表现优异。
摘要：
关系抽取（RE）即使在零样本设置下，也始终涉及一定程度的标注或未标注数据。近期研究表明，大型语言模型（LLMs）仅需给定自然语言提示即可开箱即用地良好迁移到新任务，这提供了在无任何数据和参数微调的情况下从文本中抽取关系的可能性。本工作专注于探索LLMs（如ChatGPT）作为零样本关系抽取器的研究。一方面，我们分析了现有RE提示的缺陷，并尝试引入最近的提示技术如思维链（CoT）来改进零样本RE。我们提出了总结并提问（SumAsk）提示，这是一种递归利用LLMs将RE输入转换为有效问答（QA）格式的简单提示。另一方面，我们在各种基准和设置上进行了全面实验，以调查LLMs在零样本RE上的能力。具体而言，我们有以下发现：SumAsk在不同模型规模、基准和设置上持续且显著地提升了LLMs的性能；使用ChatGPT的零样本提示与零样本和全监督方法相比取得了具有竞争力或更优的结果；LLMs在抽取重叠关系方面表现出有前景的性能；不同关系的性能差异很大。与小语言模型不同，LLMs在处理具有挑战性的以上皆非（NoTA）关系方面是有效的。
英文摘要：
Relation extraction (RE) consistently involves a certain degree of labeled or unlabeled data even if under zero-shot setting. Recent studies have shown that large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt, which provides the possibility of extracting relations from text without any data and parameter tuning. This work focuses on the study of exploring LLMs, such as ChatGPT, as zero-shot relation extractors. On the one hand, we analyze the drawbacks of existing RE prompts and attempt to incorporate recent prompt techniques such as chain-of-thought (CoT) to improve zero-shot RE. We propose the summarize-and-ask (\textsc{SumAsk}) prompting, a simple prompt recursively using LLMs to transform RE inputs to the effective question answering (QA) format. On the other hand, we conduct comprehensive experiments on various benchmarks and settings to investigate the capabilities of LLMs on zero-shot RE. Specifically, we have the following findings: (i) \textsc{SumAsk} consistently and significantly improves LLMs performance on different model sizes, benchmarks and settings; (ii) Zero-shot prompting with ChatGPT achieves competitive or superior results compared with zero-shot and fully supervised methods; (iii) LLMs deliver promising performance in extracting overlapping relations; (iv) The performance varies greatly regarding different relations. Different from small language models, LLMs are effective in handling challenge none-of-the-above (NoTA) relation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
