理性推理未必提升模型性能，有时反而降低效果。
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability
- 通过实证研究发现，添加推理过程可能削弱模型表现
- 推理增强能提升模型可靠性，尤其在难题上表现更优
- 性能与可靠性提升具线性关系，受任务难度驱动
使用推理过程增强语言模型在现有研究中被证明有益。本文通过全面调查发现，这一普遍观点并不总是成立。我们深入检验了推理对模型性能及新提出的模型可靠性的影响。结果揭示几个关键发现：1）推理有时会损害模型性能；2）推理可提升模型可靠性，甚至优于未训练的对照组；3）性能与可靠性提升存在线性对应关系，二者均受任务内在难度驱动。这些发现为推理的广泛应用提供了重要指导，并对显式对齐语言模型与隐式人类思维的过程提出深刻反思。代码可在 https://github.com/Ignoramus0817/rationales 获取。
原文摘要 · Abstract (English)
Training language models with rationales augmentation has been shown to be beneficial in many existing works. In this paper, we identify that such a prevailing view does not hold consistently. We conduct comprehensive investigations to thoroughly inspect the impact of rationales on model performance as well as a novel perspective of model reliability. The results lead to several key findings that add new insights upon existing understandings: 1) Rationales can, at times, deteriorate model performance; 2) Rationales can, at times, improve model reliability, even outperforming their untrained counterparts; 3) A linear correspondence exists in between the performance and reliability improvements, while both are driven by the intrinsic difficulty of the task. These findings provide informative regulations on the broad utilization of rationales and raise critical implications on the procedure of explicitly aligning language models with implicit human thoughts. Codes can be found at https://github.com/Ignoramus0817/rationales.
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