MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning

arXiv:2506.09105v3 Announce Type: replace Abstract: We present MetaTT, a Tensor Train (TT) adapter framework for fine-tuning of pre-trained transformers. MetaTT enables flexible and parameter-efficient model adaptation by using a single shared TT to factorize transformer sub-modules. This factorization indexes key structural dimensions, including layer and matrix type, and can optionally incorporate heads and tasks. This design allows MetaTT's parameter count to scale with the sum, rather than the product, of the modes, resulting in a substantially more compact adapter. Our benchmarks compare

Source: arXiv cs.LG — read the full report at the original publisher.

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