Optimising Hard Prompts with Few-Shot Meta-Prompting
<|reference_start|>Optimising Hard Prompts with Few-Shot Meta-Prompting: Prompting is a flexible and adaptable way of providing instructions to a Large Language Model (LLM). Contextual prompts include context in the form of a document or dialogue along with the natural language instructions to the LLM, often constraining the LLM to restrict facts to that of the given context while complying with the instructions. Masking the context, it acts as template for prompts. In this paper, we present an iterative method to generate better templates using an LLM from an existing set of prompt templates without revealing the context to the LLM. Multiple methods of optimising prompts using the LLM itself are explored to check the effect of few shot sampling methods on iterative propagation while maintaining linguistic styles and syntax on optimisation of prompt templates, yielding a 103.87% improvement using the best performing method. Comparison of the results of multiple contextual tasks demonstrate the ability of LLMs to maintain syntax while learning to replicate linguistic styles. Additionally, the effect on the output with different methods of prompt template generation is shown.<|reference_end|>
@article{hiraou2024optimising,
    title={Optimising Hard Prompts with Few-Shot Meta-Prompting},
    author={Sayash Raaj Hiraou},
    journal={arXiv preprint arXiv:2407.18920},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18920},
    primaryClass={cs.CL}
}