Data-efficient Fine-tuning for LLM-based Recommendation
CoRR(2024)
摘要
Leveraging Large Language Models (LLMs) for recommendation has recently
garnered considerable attention, where fine-tuning plays a key role in LLMs'
adaptation. However, the cost of fine-tuning LLMs on rapidly expanding
recommendation data limits their practical application. To address this
challenge, few-shot fine-tuning offers a promising approach to quickly adapt
LLMs to new recommendation data. We propose the task of data pruning for
efficient LLM-based recommendation, aimed at identifying representative samples
tailored for LLMs' few-shot fine-tuning. While coreset selection is closely
related to the proposed task, existing coreset selection methods often rely on
suboptimal heuristic metrics or entail costly optimization on large-scale
recommendation data.
To tackle these issues, we introduce two objectives for the data pruning task
in the context of LLM-based recommendation: 1) high accuracy aims to identify
the influential samples that can lead to high overall performance; and 2) high
efficiency underlines the low costs of the data pruning process. To pursue the
two objectives, we propose a novel data pruning method based on two scores,
i.e., influence score and effort score, to efficiently identify the influential
samples. Particularly, the influence score is introduced to accurately estimate
the influence of sample removal on the overall performance. To achieve low
costs of the data pruning process, we use a small-sized surrogate model to
replace LLMs to obtain the influence score. Considering the potential gap
between the surrogate model and LLMs, we further propose an effort score to
prioritize some hard samples specifically for LLMs. Empirical results on three
real-world datasets validate the effectiveness of our proposed method. In
particular, the proposed method uses only 2
fine-tuning, reducing time costs by 97
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