PASER: Post-Training Data Selection for Efficient Pruned Large Language Model Recovery

• URL: http://arxiv.org/abs/2502.12594v2
• Date: Fri, 23 May 2025 06:20:09 GMT
• Title: PASER: Post-Training Data Selection for Efficient Pruned Large Language Model Recovery
• Authors: Bowei He, Lihao Yin, Hui-Ling Zhen, Xiaokun Zhang, Mingxuan Yuan, Chen Ma,
• Abstract summary: Post-training techniques such as instruction tuning are commonly employed to recover model performance.<n>However, some irrelevant instructions may also introduce negative effects to model capacity recovery.<n>We propose textbfPost-training dtextbfAta textbfSelection method for textbfEfficient pruned large language model textbfRecovery (textbfPASER)
• Score: 11.20326903218271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model pruning is an effective approach for compressing large language models (LLMs). However, this process often leads to significant degradation of model capabilities. While post-training techniques such as instruction tuning are commonly employed to recover model performance, existing methods often overlook the uneven deterioration of model capabilities and incur high computational costs. Moreover, some irrelevant instructions may also introduce negative effects to model capacity recovery. To address these challenges, we propose the \textbf{P}ost-training d\textbf{A}ta \textbf{S}election method for \textbf{E}fficient pruned large language model \textbf{R}ecovery (\textbf{PASER}). PASER aims to identify instructions to recover the most compromised model capacities with a certain data budget. Our approach first applies manifold learning and spectral clustering to group recovery instructions in the semantic space, revealing capability-specific instruction sets. Then, the data budget is adaptively allocated across clusters by the degree of corresponding model capability degradation. In each cluster, we prioritize data samples that lead to the most decline of model performance. To mitigate potential negative tuning effects, we also detect and filter out conflicting or irrelevant recovery data. Extensive experiments demonstrate that PASER significantly outperforms conventional baselines, effectively recovering the general capabilities of pruned LLMs while utilizing merely 4\%-20\% of the original post-training data. We provide the anonymous code repository in \href{https://anonymous.4open.science/r/PASER-E606}{Link}.

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