Two Failures Of Self-consistency In The Multi-step Reasoning Of Llms

Chen Angelica, Phang Jason, Parrish Alicia, Padmakumar Vishakh, Zhao Chen, Bowman Samuel R., Cho Kyunghyun. Transactions on Machine Learning Research 2023

[Paper]    
Few Shot GPT Model Architecture Reinforcement Learning

Large language models (LLMs) have achieved widespread success on a variety of in-context few-shot tasks, but this success is typically evaluated via correctness rather than consistency. We argue that self-consistency is an important criteria for valid multi-step reasoning in tasks where the solution is composed of the answers to multiple sub-steps. We propose two types of self-consistency that are particularly important for multi-step reasoning – hypothetical consistency (a model’s ability to predict what its output would be in a hypothetical other context) and compositional consistency (consistency of a model’s final outputs when intermediate sub-steps are replaced with the model’s outputs for those steps). We demonstrate that multiple variants of the GPT-3/-4 models exhibit poor consistency rates across both types of consistency on a variety of tasks.

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