Computer Science > Artificial Intelligence
[Submitted on 22 Nov 2023 (v1), last revised 7 Oct 2024 (this version, v5)]
Title:AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations
View PDFAbstract: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. The code and data can be found at this https URL.
Submission history
From: Zhicheng Yang [view email][v1] Wed, 22 Nov 2023 17:24:21 UTC (8,159 KB)
[v2] Wed, 10 Jan 2024 14:16:41 UTC (8,669 KB)
[v3] Sat, 27 Jan 2024 10:10:20 UTC (1,102 KB)
[v4] Sat, 13 Jul 2024 13:36:09 UTC (1,392 KB)
[v5] Mon, 7 Oct 2024 09:11:49 UTC (1,392 KB)
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