基于Logit对比的思维链增强以提升语言模型的推理能力
Chain-of-Thought Augmentation with Logit Contrast for Enhanced Reasoning in Language Models
AI总结：
为提升语言模型推理与组合泛化能力，受思维链提示与上下文感知解码启发，探索基于输入的对比方法以鼓励思维链推理，并证实其改进值得进一步研究。
摘要：
模型规模的迅速扩大结合思维链提示等引导方法，使语言模型推理能力大幅提升。同时，模型在组合泛化方面存在困难，且在许多基于推理的基准上远不及人类表现。借助思维链提示的成功，并从上下文感知解码（CAD）中汲取灵感，我们探索了基于输入的对比方法，以进一步鼓励思维链提示所诱导的推理类型。尽管在跨数据集和模型稳定这些结果方面仍有工作要做，但我们发现的改进值得进一步研究用于上下文感知推理的基于输入的引导方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
