Contrastive Decoding Improves Reasoning in Large Language Models
Authors: name: Sean O'Brien, name: Mike Lewis

We demonstrate that Contrastive Decoding -- a simple, computationally light,
and training-free text generation method proposed by Li et al 2022 -- achieves
large out-of-the-box improvements over greedy decoding on a variety of
reasoning tasks. Originally shown to improve the perceived quality of long-form
text generation, Contrastive Decoding searches for strings that maximize a
weighted difference in likelihood between strong and weak models. We show that
Contrastive Decoding leads LLaMA-65B to outperform LLaMA 2, GPT-3.5 and PaLM
2-L on the HellaSwag commonsense reasoning benchmark, and to outperform LLaMA
2, GPT-3.5 and PaLM-540B on the GSM8K math word reasoning benchmark, in
addition to improvements on a collection of other tasks. Analysis suggests that
Contrastive Decoding improves over existing methods by preventing some abstract
reasoning errors, as well as by avoiding simpler modes such as copying sections
of the input during chain-of-thought. Overall, Contrastive Decoding outperforms
nucleus sampling for long-form generation and greedy decoding for reasoning
tasks, making it a powerful general purpose method for generating text from
language models.