title: Contrastive Decoding Improves Reasoning in Large Language Models
summary: 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.
paper_authors._id: 65090e347ee07e274b261838
paper_authors.name: Sean O'Brien
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-09-19T02:57:59.174Z
paper_authors._id: 64b6e431e4fd165b0bb0dce2
paper_authors.avatarUrl: /avatars/fd7fd8bb3e892d401f721d1bbc13c183.svg
paper_authors.fullname: Sean O'Brien
paper_authors.type: user
paper_authors.user: seanobrienresearch
paper_authors._id: 65090e347ee07e274b261839
paper_authors.name: Mike Lewis
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-20T13:12:16.128Z
paper_authors._id: 5e67eda71009063689407484
paper_authors.avatarUrl: /avatars/6d7688faba888d463584eaa02ad011e3.svg
paper_authors.fullname: Mike Lewis
paper_authors.type: user
paper_authors.user: mikelewis0
paper_title: Contrastive Decoding Improves Reasoning in Large Language Models
paper_summary: 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.
paper_ai_summary: Contrastive Decoding, a simple and training-free text generation method, outperforms other decoding techniques on various reasoning tasks and logical benchmarks by improving perceived quality and reducing reasoning errors.
paper_ai_keywords: Contrastive Decoding greedy decoding reasoning tasks HellaSwag GSM8K chain-of-thought nucleus sampling