LoRTA: Low Rank Tensor Adaptation of Large Language Models

ICLR 2025 Conference Submission12241 Authors

Keywords: PEFT, LLM, Fine-tuning, Efficiency, Low-rank, Tensors

TL;DR: Low Rank Tensor Adapters for efficient LLM finetuning

Abstract: Low Rank Adaptation (LoRA) is a popular Parameter Efficient Fine Tuning (PEFT) method that effectively adapts large pre-trained models for downstream tasks. LoRA parameterizes model updates using low-rank matrices at each layer, significantly reducing the number of trainable parameters and, consequently, resource requirements during fine-tuning. However, the lower bound on the number of trainable parameters remains high due to the use of the low-rank matrix model. In this paper, we address this limitation by proposing a novel approach that employs a low rank tensor parametrization for model updates. The proposed low rank tensor model can significantly reduce the number of trainable parameters, while also allowing for finer-grained control over adapter size. Our experiments on Natural Language Understanding, Instruction Tuning, Preference Optimization and Protein Folding benchmarks demonstrate that our method is both efficient and effective for fine-tuning Large Language Models, achieving a reduction in the number of parameters while maintaining comparable performance.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 12241