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Computer Science > Computation and Language

arXiv:2309.09117 (cs)
[Submitted on 17 Sep 2023]

Title:Contrastive Decoding Improves Reasoning in Large Language
Models

Authors:Sean O'Brien, Mike Lewis
Download a PDF of the paper titled Contrastive Decoding Improves
Reasoning in Large Language Models, by Sean O'Brien and 1 other
authors
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    Abstract: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.

Comments:    10 figures, 13 tables
Subjects:    Computation and Language (cs.CL); Artificial
             Intelligence (cs.AI)
ACM classes: I.2.7
Cite as:     arXiv:2309.09117 [cs.CL]
             (or arXiv:2309.09117v1 [cs.CL] for this version)
             https://doi.org/10.48550/arXiv.2309.09117
             Focus to learn more
             arXiv-issued DOI via DataCite

Submission history

From: Sean O'Brien [view email]
[v1] Sun, 17 Sep 2023 00:29:32 UTC (1,557 KB)
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