Prompt Selection and Augmentation for Few Examples Code Generation in Large Language Model and its Application in Robotics Control
<|reference_start|>Prompt Selection and Augmentation for Few Examples Code Generation in Large Language Model and its Application in Robotics Control: 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 the question. When combined with the Program-of-Thought prompting, our algorithm demonstrates an improvement in performance on the GSM8K and SVAMP benchmarks, with increases of 0.3% and 1.1% respectively. Furthermore, in simulated tabletop environments, our algorithm surpasses the Code-as-Policies approach by achieving a 3.4% increase in successful task completions and a decrease of over 70% in the number of examples used. Its ability to discard examples that contribute little to solving the problem reduces the inferencing time of an LLM-powered robotics system. This algorithm also offers important benefits for industrial process automation by streamlining the development and deployment process, reducing manual programming effort, and enhancing code reusability.<|reference_end|>
@article{wu2024prompt,
    title={Prompt Selection and Augmentation for Few Examples Code Generation in
  Large Language Model and its Application in Robotics Control},
    author={On Tai Wu, Frodo Kin Sun Chan, Zunhao Zhang, Yan Nei Law, Benny
  Drescher and Edmond Shiao Bun Lai},
    journal={arXiv preprint arXiv:2403.12999},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12999},
    primaryClass={cs.RO cs.AI cs.CL cs.LG}
}