AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations
- URL: http://arxiv.org/abs/2311.13538v4
- Date: Sat, 13 Jul 2024 13:36:09 GMT
- Title: AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations
- Authors: Zhicheng Yang, Yinya Huang, Jing Xiong, Liang Feng, Xiaodan Liang, Yiwei Wang, Jing Tang,
- Abstract summary: AlignedCoT is an in-context learning technique for invoking Large Language Models.
It achieves consistent and correct step-wise prompts in zero-shot scenarios.
We conduct experiments on mathematical reasoning and commonsense reasoning.
- Score: 52.43593893122206
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Large Language Models prompting, such as using in-context demonstrations, is a mainstream technique for invoking LLMs to perform high-performance and solid complex reasoning (e.g., mathematical reasoning, commonsense reasoning), and has the potential for further human-machine collaborative scientific findings. However, current LLMs are delicate and elusive in prompt words and styles. And there is an unseen gap between LLM understanding and human-written prompts. This paper introduces AlignedCoT, an LLM-acquainted prompting technique that includes proficient "native-speaking" in in-context learning for the LLMs. Specifically, it achieves consistent and correct step-wise prompts in zero-shot scenarios by progressively probing, refining, and formatting the LLM chain of thoughts so that free from handcrafted few-shot demonstrations while maintaining the prompt quality. We conduct experiments on mathematical reasoning and commonsense reasoning. We find that LLMs with AlignedCoT perform significantly superior to them with human-crafted demonstrations. We further apply AlignedCoT for rewriting the GSM8k training set, resulting in a GSM8k-Align dataset. We observe its benefits for retrieval augmented generation.
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