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

TL;DR

PASER is a novel post-training data selection method that efficiently recovers pruned large language models by identifying and prioritizing the most impactful instruction data, reducing data usage and negative effects.

Contribution

PASER introduces a capability-aware data selection framework using manifold learning and spectral clustering to improve model recovery efficiency after pruning.

Findings

Outperforms baseline methods in recovering model capabilities.

Achieves effective recovery with only 4-20% of original data.

Reduces negative effects by filtering irrelevant data.

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 code repository in \href{https://github.com/BokwaiHo/PASER}{Link}.