不要使用LLM来作出相关性判断
1National Institute of Standards and Technology, Gaithersburg, Maryland, USA.
概括
大型语言模型 (LLM) 不应该为信息检索 (IR) 评估产生相关性判断. 使用LLM作为代理程序创建了性能上限,阻碍了检索系统的准确评估.
科学领域:
- 信息检索 信息检索
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 相关性判断对于评估信息检索 (IR) 系统至关重要.
- 手动创建相关性数据是耗时和资源密集的.
- 大型语言模型 (LLM) 提供了在IR中自动化任务的潜力.
研究的目的:
- 调查使用LLM来产生IR评估相关性判断的可行性和影响.
- 确定LLM是否可以作为人类法官在评估信息相关性的可靠代理.
- 确定将LLM整合到相关性评估过程中的最佳实践.
主要方法:
- 这项研究批判性地分析了使用LLMs在IR中生成真实数据的概念.
- 它讨论了LLM产生的判断所带来的固有局限性和潜在偏见.
- 它探讨了在相关性评估中整合LLM的替代,更合适的方法.
主要成果:
- 直接使用LLM来产生相关性判断的人工地限制了评估的IR系统的性能上限.
- 由LLM生成的真实数据可能导致有缺陷和不可靠的IR评估.
- 这种方法破坏了严格的系统评估的目的.
结论:
- 不应使用LLM来直接产生IR评估的相关性判断.
- 雇用LLM作为人类法官的直接代理人,对评估的完整性有损.
- 在IR中,LLM可以是有价值的工具,但它们在生成基本真相方面的应用需要仔细考虑和替代方法.
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