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Transformer-based Large Language Models (LLMs) have become a fixture inmodern machine learning. Correspondingly, significant resources are allocatedtowards research that aims to further advance this technology, typicallyresulting in models of increasing size that are trained on increasing amountsof data. This work, however, demonstrates the surprising result that it isoften possible to significantly improve the performance of LLMs by selectivelyremoving higher-order components of their weight matrices. This simpleintervention, which we call LAyer-SElective Rank reduction (LASER), can be doneon a model after training has completed, and requires no additional parametersor data. We show extensive experiments demonstrating the generality of thisfinding across language models and datasets, and provide in-depth analysesoffering insights into both when LASER is effective and the mechanism by whichit operates.

The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction