@inproceedings{
xu2023latent,
title={Latent Skill Discovery for Chain-of-Thought Reasoning},
author={Zifan Xu and Haozhu Wang and Dmitriy Bespalov and Peter Stone and Yanjun Qi},
booktitle={R0-FoMo:Robustness of Few-shot and Zero-shot Learning in Large Foundation Models},
year={2023},
url={https://openreview.net/forum?id=a3ZQVXD0Hv}
}
Recent advances in Large Language Models (LLMs) have led to an emergent ability of chain-of-thought (CoT) prompting, a prompt reasoning strategy that adds intermediate rationale steps between questions and answers to construct prompts. Conditioned on these prompts, LLMs can effectively learn in context to generate rationales that lead to more accurate answers than when answering the same question directly. To design LLM prompts, one important setting, called demonstration selection, considers selecting demonstrations from an example bank. Existing methods use various heuristics for this selection, but for CoT prompting, which involves unique rationales, it is essential to base the selection upon the intrinsic skills that CoT rationales need, for instance, the skills of addition or subtraction for math word problems.

To address this requirement, we introduce a novel approach named Reasoning Skill Discovery (RSD) that uses unsupervised learning to create a latent space representation of rationales, called a reasoning skill. Simultaneously, RSD learns a reasoning policy to determine the required reasoning skill for a given question. This can then guide the selection of examples that demonstrate the required reasoning skills. Our approach offers several desirable properties: it is (1) theoretically grounded, (2) sample-efficient, requiring no LLM inference or manual prompt design, and (3) LLM-agnostic. Empirically, RSD outperforms existing methods by up to 6% in terms of the answer accuracy across multiple reasoning tasks.
Latent Skill Discovery for Chain-of-Thought Reasoning
Zifan Xu Haozhu Wang Dmitriy Bespalov Peter Stone Yanjun Qi