Prompt Selection and Augmentation for Few Examples Code Generation in Large Language Model and its Application in Robotics Control

arXiv:2403.12999v1 Announce Type: cross
Abstract: Few-shot prompting and step-by-step reasoning have enhanced the capabilities of Large Language Models (LLMs) in tackling complex tasks including code generation. In this paper, we introduce a prompt selection and augmentation algorithm aimed at improving mathematical reasoning and robot arm operations. Our approach incorporates a multi-stage example augmentation scheme combined with an example selection scheme. This algorithm improves LLM performance by selecting a set of examples that increase diversity, minimize redundancy, and increase relevance to …

abstract algorithm application arxiv augmentation capabilities code code generation control cs.ai cs.cl cs.lg cs.ro examples few-shot improving language language model language models large language large language model large language models llms paper prompt prompting reasoning robotics step-by-step tasks type