Prompt Selection and Augmentation for Few Examples Code Generation in
  Large Language Model and its Application in Robotics Control
On Tai Wu Frodo Kin Sun Chan Zunhao Zhang Yan Nei Law Benny Drescher Edmond Shiao Bun Lai
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.