SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning

arXiv:2405.00705v1 Announce Type: new
Abstract: The pre-trained Large Language Models (LLMs) can be adapted for many downstream tasks and tailored to align with human preferences through fine-tuning. Recent studies have discovered that LLMs can achieve desirable performance with only a small amount of high-quality data, suggesting that a large amount of the data in these extensive datasets is redundant or even harmful. Identifying high-quality data from vast datasets to curate small yet effective datasets has emerged as a critical challenge. …

abstract arxiv automated cs.cl cs.lg data dataset fine-tuning human language language models large language large language models llms performance quality quality data small studies tasks through type