  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.
