在大型语言模型时代重新审视关系提取
Somin Wadhwa1, Silvio Amir1, Byron C Wallace1
1Northeastern University.
概括
大型语言模型显示出关系提取 (RE) 的前景. 使用GPT-3的Few-Shot提示接近最先进的状态,而使用链式思维解释微调的Flan-T5实现了最高的结果.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 关系提取 (RE) 是NLP的一个关键任务,用于识别文本中实体之间的语义关系.
- 传统的RE方法涉及对实体和关系识别的监督学习.
- 最近的进展探索了RE的序列对序列模型,以文本形式生成关系.
研究的目的:
- 评估大型语言模型 (LLM) 如GPT-3和Flan-T5在RE任务上的性能.
- 调查不同监管水平对生成性可再生能源方法的影响.
- 通过结合人类评估来完善生成性RE的评估方法.
主要方法:
- 使用大型语言模型 (GPT-3,Flan-T5大型) 作为一个序列对序列任务来提取关系.
- 采用了少量射击提示和监督的微调策略.
- 由GPT-3生成的内置的思维链 (CoT) 解释,用于微调Flan-T5.5.
- 进行人体评估,以评估超出精确匹配指标的表现.
主要成果:
- 通过使用GPT-3实现了与完全监督模型相提并论的性能.
- 虽然Flan-T5在少数镜头设置中效率较低,但在微调后与CoT解释后达到最先进的结果.
- 人类评估提供了更细致的评估生成RE性能.
结论:
- 大型语言模型,特别是具有几次拍摄提示的GPT-3,为关系提取提供了强大的替代方案.
- 思想链微调可以提高像Flan-T5这样的模型在RE任务中的性能.
- 该研究引入了新的RE基线模型,并提倡改进评估方法.
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