2023/12/03 by Vali Tawosi, Salwa Alamir, Xiaomo Liu · 1 voice · 1 citation
Computer Science · #Machine Learning and Data Classification #Natural Language Processing Techniques #Topic Modeling #cs.AI #cs.SE
paper · pdf · doi:10.1007/978-3-031-48796-5_9
openalex publication_date 2023/12/03 · openalex created_date 2023/12/04 · arxiv published 2024/03/13 · arxiv updated 2024/03/13 · openalex updated_date 2026/07/29
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.