Transformer-based Large Language Models (LLMs) have become a fixture in modern machine learning. Correspondingly, significant resources are allocated towards research that aims to further advance this technology, typically resulting in models of increasing size that are trained on increasing amounts of data. This work, however, demonstrates the surprising result that it is often possible to im-prove the performance of LLMs by simply removing higher-order components of their constituent weight matrices in the multi-layer perception (MLP) layers. This simple intervention, which we call LAyer-SElective Rank reduction (LASER), can be done on a model after training has completed, and requires no additional parameters or data. LASER can dramatically boost predictive performance—at times by 80% over the model’s original performance—on question-answering tasks and across various modalities for which Transformers are used.
The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
Pratyusha Sharma Jordan T. Ash Dipendra Misra