DEEP THINK WITH CONFIDENCE
Descripción general
Resumen del artículo
This paper introduces Deep Think with Confidence (DeepConf), a method to make large language models solve reasoning tasks more efficiently. DeepConf leverages the model's internal confidence to filter out unlikely reasoning paths, either during the generation process or afterward. Experiments on various benchmarks and LLMs show it maintains or improves accuracy while substantially reducing token generation.
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This paper introduces a way to make large language models think more efficiently, like discarding bad ideas early on instead of fully exploring them. This saves time and computing power while keeping accuracy high.
Posibles conflictos de intereses
The authors have affiliations with Meta AI and UCSD. Part of the work was done during an internship at Meta FAIR. This could represent a potential conflict of interest.
Limitaciones identificadas
Explicación de la calificación
This paper presents a novel and practical approach (DeepConf) to improve the efficiency of large language models during test time. The method is simple yet effective, demonstrating significant token reduction and accuracy improvement across several benchmarks and models. While the method shows promising results, there are some limitations regarding confidence calibration and the evaluation methodology that prevent a perfect score.
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