一致性(知识库)
计算机科学
基本事实
语言模型
人工智能
自然语言处理
机器学习
作者
Jiaxin Huang,Shixiang Gu,Le Hou,Yuexin Wu,Xuezhi Wang,Hongkun Yu,Jiawei Han
出处
期刊:Cornell University - arXiv
日期:2022-01-01
被引量:15
标识
DOI:10.48550/arxiv.2210.11610
摘要
Large Language Models (LLMs) have achieved excellent performances in various tasks. However, fine-tuning an LLM requires extensive supervision. Human, on the other hand, may improve their reasoning abilities by self-thinking without external inputs. In this work, we demonstrate that an LLM is also capable of self-improving with only unlabeled datasets. We use a pre-trained LLM to generate "high-confidence" rationale-augmented answers for unlabeled questions using Chain-of-Thought prompting and self-consistency, and fine-tune the LLM using those self-generated solutions as target outputs. We show that our approach improves the general reasoning ability of a 540B-parameter LLM (74.4%->82.1% on GSM8K, 78.2%->83.0% on DROP, 90.0%->94.4% on OpenBookQA, and 63.4%->67.9% on ANLI-A3) and achieves state-of-the-art-level performance, without any ground truth label. We conduct ablation studies and show that fine-tuning on reasoning is critical for self-improvement.
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