在数据集和模型中对思维链推理策略的比较
Konstantin Hebenstreit1, Robert Praas2, Louis P Kiesewetter3
1Medical University of Vienna, Center for Medical Data Science, Institute of Artificial Intelligence, Vienna, Austria.
PeerJ. Computer science
|June 10, 2024
概括
思想链 (CoT) 推理可以改进大型语言模型 (LLM). 这项研究发现,在新一代的LLM和多样化的数据集中,CoT策略是强大的,提高了性能和可解释性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 在大型语言模型 (LLM) 中出现的思维链 (CoT) 推理为提高性能和可解释性提供了潜力.
- 关于在不同LLM代和数据集中对CoT策略的概括性存在不确定性.
研究的目的:
- 为了比较各种零射击的有效性,促使COT推理策略.
- 评估六个最近的LLM和六个不同的问答数据集的战略概括性.
主要方法:
- 用不同的 CoT 推理策略进行零射击提示.
- 在六个LLM中进行测试:davinci-002,davinci-003,GPT-3.5-turbo,GPT-4,Flan-T5-xxl和Cohere命令-xlarge.
- 评估六个问题答案数据集,需要现实世界知识和逻辑口头推理,包括科学和医学领域.
主要成果:
- 尽管存在微小的差异,但CoT推理策略在各种LLM和数据集中表现出强大的有效性.
- GPT-4显示出最先进的推理策略的最大好处,特别是在使用自动发现提示符时.
- 该研究证实了Cot推理的广泛适用性和一致的好处.
结论:
- CoT推理策略是提高LLM绩效和可解释性的可靠方法.
- GPT-4表现出对先进推理技术的卓越适应.
- 进一步的研究可以探索针对各种LLM架构的优化CoT快速发现.
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