Small Language Model as Data Prospector for Large Language Model

• URL: http://arxiv.org/abs/2412.09990v1
• Date: Fri, 13 Dec 2024 09:23:58 GMT
• Title: Small Language Model as Data Prospector for Large Language Model
• Authors: Shiwen Ni, Haihong Wu, Di Yang, Qiang Qu, Hamid Alinejad-Rokny, Min Yang,
• Abstract summary: textttNUGGETS identifies and selects high-quality quality data from a large dataset.<n>textttSuperNUGGETS uses a small language model (SLM) instead of a large language model (LLM) to filter the data for outstanding one-shot instances.<n>The experimental results show that the performance of textttSuperNUGGETS only decreases by 1-2% compared to textttNUGGETS, but the efficiency can be increased by a factor of 58.
• Score: 22.659698878699032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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