GIFT: Generative Interpretable Fine-tuning
Savadikar Chinmay, Song Xi, Wu Tianfu. Arxiv 2023
[Paper]
We present Generative Interpretable Fine-Tuning (GIFT) for parameter-efficient fine-tuning of pretrained Transformer backbones, which can be formulated as a simple factorized matrix multiplication in the parameter space or equivalently in the activation/representation space, and thus embraces built-in interpretability. For a layer with weights \(\omega\in \mathbb{R}^{d_{out}\times d_{in}}\), our proposed GIFT learns the fine-tuned weights \(\hat{\omega}\) directly from \(\omega\) as \(\hat{\omega}=\omega\cdot (\mathbb{I}+\phi_{d_{in}\times r}\cdot\psi_{r\times d_{in}})\). \(\Theta=(\phi, \psi)\) are the learnable parameters of the two linear layers. \(\Theta\) can be shared by all layers selected for fine-tuning (e.g., all the Query and Value layers), or can be layer-type specific (e.g., different \(\Theta\)’s used for Query and Value), resulting in significantly fewer trainable parameters compared to layer-specific Low-Rank Adaptation (LoRA). We perform comprehensive evaluations on natural language tasks (commonsense and arithmetic reasoning, instruction tuning, and sequence classification), and fine-grained visual classification tasks. We obtain the best performance and parameter efficiency among baselines on commonsense reasoning, instruction tuning and visual recognition benchmarks. Compared to LoRA, we obtain 5.9% absolute increase in average accuracy with 53.8 times reduction of parameters on Commonsense170k using Llama-3 (8B), and 5.4% absolute increase in the win rate with 4 times reduction of parameters using Llama-2 (7B) during instruction tuning. Our GIFT also obtains a slightly higher win rate on instruction tuning than GPT 3.5 (Turbo 1106). We show the output of the first linear layer (i.e., \(\omega\cdot \phi\)) is surprisingly interpretable, which can play the role of a token-clustering head as a by-product to localize meaningful objects/parts in images for computer vision tasks.
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