arXiv Machine Learning By Josh McGiff, Salma Mekaoui, Robert Shanahan, Nikola S. Nikolov

SemiAdapt-Instruct: Extensible Instruction Tuning via Latent Domain-Specialised Adapters

Read the original on arXiv Machine Learning →

arXiv:2608. 05161v1 Announce Type: cross Abstract: Instruction-tuned LLMs are deployed into environments where domains evolve, yet extending a fine-tuned model's capabilities without full retraining remains an unsolved practical challenge.

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arXiv AI
Aug 25

From Isolation to Alignment: Unified LoRA for Efficient Multi-Task Learning

The paper introduces Align‑LoRA, a unified LoRA framework for multi‑task learning that replaces complex, isolated adapter designs with a single‑adapter model enhanced by a higher rank and an explicit alignment loss. It demonstrates that a router‑free, multi‑head model with high inter‑head redundancy can outperform more elaborate baselines, and that a unified LoRA can achieve competitive performance while enabling weight merging and zero inference latency. Extensive experiments and theoretical analysis confirm that Align‑LoRA surpasses prevailing approaches, offering a simpler, production‑friendly paradigm for parameter‑efficient fine‑tuning of large language models.

By Jinda Liu, Yi Chang, Yuan Wu