教小型语言模型推理
Teaching Small Language Models to Reason
AI总结：
本文提出用大型教师模型生成的思维链微调小型学生模型，通过知识蒸馏将推理能力迁移到千亿参数以下模型，并显著提升多类推理任务表现。
摘要：
思维链提示成功提升了大型语言模型的推理能力，并在一系列数据集上取得了最先进的结果。然而，这些推理能力似乎只出现在参数规模超过1000亿的模型中。本文探索通过知识蒸馏将此类推理能力迁移到参数少于1000亿的模型。具体而言，我们在更大的教师模型生成的思维链输出上微调学生模型。实验表明，所提方法提升了算术、常识和符号推理数据集上的任务表现。例如，当在PaLM-540B生成的思维链上进行微调时，T5 XXL在GSM8K上的准确率从8.11%提高到21.99%。
英文摘要：
Chain of thought prompting successfully improves the reasoning capabilities of large language models, achieving state of the art results on a range of datasets. However, these reasoning capabilities only appear to emerge in models with a size of over 100 billion parameters. In this paper, we explore the transfer of such reasoning capabilities to models with less than 100 billion parameters via knowledge distillation. Specifically, we finetune a student model on the chain of thought outputs generated by a larger teacher model. Our experiments show that the proposed method improves task performance across arithmetic, commonsense and symbolic reasoning datasets. For example, the accuracy of T5 XXL on GSM8K improves from 8.11% to 21.99% when finetuned on PaLM-540B generated chains of thought.
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。
