Small Language Model as Data Prospector for Large Language Model

TL;DR

This paper introduces SuperNUGGETS, an efficient data filtering method using a small language model to select high-quality instruction data for fine-tuning large language models, achieving comparable performance with much lower resource use.

Contribution

SuperNUGGETS improves data selection efficiency for LLM fine-tuning by replacing a large model with a small model, reducing resource consumption while maintaining performance.

Findings

Performance decreases by only 1-2% compared to NUGGETS.

Efficiency increases by a factor of 58.

Higher utility value due to lower resource consumption.

Abstract

The quality of instruction data directly affects the performance of fine-tuned Large Language Models (LLMs). Previously, \cite{li2023one} proposed \texttt{NUGGETS}, which identifies and selects high-quality quality data from a large dataset by identifying those individual instruction examples that can significantly improve the performance of different tasks after being learnt as one-shot instances. In this work, we propose \texttt{SuperNUGGETS}, an improved variant of \texttt{NUGGETS} optimised for efficiency and performance. Our \texttt{SuperNUGGETS} uses a small language model (SLM) instead of a large language model (LLM) to filter the data for outstanding one-shot instances and refines the predefined set of tests. The experimental results show that the performance of \texttt{SuperNUGGETS} only decreases by 1-2% compared to \texttt{NUGGETS}, but the efficiency can be increased by a factor of 58. Compared to the original \texttt{NUGGETS}, our \texttt{SuperNUGGETS} has a higher utility value due to the significantly lower resource consumption.