Chain-of-Thought Augmentation with Logit Contrast for Enhanced Reasoning in Language Models
This work addresses reasoning limitations in language models for AI applications, but it is incremental as it builds on existing chain-of-thought prompting and context-aware decoding methods.
The paper tackles the problem of compositional generalization in language models by exploring input-based contrasting methods to enhance reasoning, resulting in improvements that warrant further investigation.
Rapidly increasing model scales coupled with steering methods such as chain-of-thought prompting have led to drastic improvements in language model reasoning. At the same time, models struggle with compositional generalization and are far from human performance on many reasoning-based benchmarks. Leveraging the success of chain-of-thought prompting, and also taking inspiration from context-aware decoding (CAD), we explore input-based contrasting methods to further encourage the type of reasoning induced by chain-of-thought prompting. While work remains to stabilize these results across datasets and models, the improvements we find warrant further investigation into input-based steering methods for context-aware reasoning.