Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

• URL: http://arxiv.org/abs/2310.06117v2
• Date: Tue, 12 Mar 2024 04:38:27 GMT
• Title: Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
• Authors: Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, Heng-Tze Cheng, Ed H. Chi, Quoc V Le and Denny Zhou
• Abstract summary: Step-Back Prompting enables LLMs to do abstractions to derive high-level concepts and first principles from instances containing specific details. Using the concepts and principles to guide reasoning, LLMs significantly improve their abilities in following a correct reasoning path towards the solution.
• Score: 122.19845578690466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Step-Back Prompting, a simple prompting technique that enables LLMs to do abstractions to derive high-level concepts and first principles from instances containing specific details. Using the concepts and principles to guide reasoning, LLMs significantly improve their abilities in following a correct reasoning path towards the solution. We conduct experiments of Step-Back Prompting with PaLM-2L, GPT-4 and Llama2-70B models, and observe substantial performance gains on various challenging reasoning-intensive tasks including STEM, Knowledge QA, and Multi-Hop Reasoning. For instance, Step-Back Prompting improves PaLM-2L performance on MMLU (Physics and Chemistry) by 7% and 11% respectively, TimeQA by 27%, and MuSiQue by 7%.

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