Search-based Optimisation of LLM Learning Shots for Story Point Estimation
<|reference_start|>Search-based Optimisation of LLM Learning Shots for Story Point Estimation: One of the ways Large Language Models (LLMs) are used to perform machine learning tasks is to provide them with a few examples before asking them to produce a prediction. This is a meta-learning process known as few-shot learning. In this paper, we use available Search-Based methods to optimise the number and combination of examples that can improve an LLM's estimation performance, when it is used to estimate story points for new agile tasks. Our preliminary results show that our SBSE technique improves the estimation performance of the LLM by 59.34% on average (in terms of mean absolute error of the estimation) over three datasets against a zero-shot setting.<|reference_end|>
@article{tawosi2024search-based,
    title={Search-based Optimisation of LLM Learning Shots for Story Point
  Estimation},
    author={Vali Tawosi, Salwa Alamir, Xiaomo Liu},
    journal={Search-Based Software Engineering. SSBSE 2023. Lecture Notes in
  Computer Science, vol 14415. Springer},
    year={2024},
    doi={10.1007/978-3-031-48796-5_9},
    archivePrefix={arXiv},
    eprint={2403.08430},
    primaryClass={cs.SE cs.AI}
}