Hugging Face Trending Papers
AutoAdapt: Automatic Domain Discovery Enables Low-Cost Extensibility
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arXiv Machine Learning
Oct 8
AutoAdapt: Automatic Domain Discovery Enables Low-Cost Extensibility
AutoAdapt is a modular framework that automatically discovers latent domains and trains per-domain Low‑Rank Adaptation (LoRA) adapters independently in parallel, without modifying other adapters. It uses parameter‑free routing to integrate new domains, achieving performance comparable to a LoRA adapter trained on all domains while avoiding full‑model retraining. The approach prevents domain interference by construction, enabling taxonomy‑free domain specialization across 14 benchmarks and GPT‑4o evaluations.
arXiv Machine Learning
Aug 7
SemiAdapt-Instruct: Extensible Instruction Tuning via Latent Domain-Specialised Adapters
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.
arXiv AI
6d ago
Upcycling Language Models for Multiple Specialized Domains
The paper introduces Domain‑Specialized Upcycling (DS‑Upcycling), a method that expands a dense Transformer with domain‑specific experts and a router to improve multi‑domain adaptation. Unlike prior LoRA‑based adapters that either weight all domains equally or activate all experts, DS‑Upcycling trains a domain‑aware router to select the appropriate expert while freezing other parameters. Experiments demonstrate superior average performance across domains and high routing accuracy (90‑98%) with only 256 examples per domain.
arXiv AI
Aug 5
Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment
arXiv:2407. 21311v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data.
arXiv Machine Learning
Aug 18
Self-Routed Tensor Adapters for Parameter-Efficient Universal Visual Adaptation
arXiv:2608. 16384v1 Announce Type: cross Abstract: Universal visual representations require adaptation mechanisms that adapt across heterogeneous domains without fragmenting knowledge into domain-specific modules.
arXiv Computation and Language
4d ago
HyperRepair: Recovering Forgotten General Capabilities of Fine-Tuned Language Models
HyperRepair is a framework that restores the general capabilities of fine‑tuned language models while keeping their domain specialization, using only inference time. It learns a hypernetwork from repairs on source models, which generates a Repairing Side Net (RSN) that selectively corrects the model based on activation fingerprints. Evaluated on two model families, 12 LoRA adaptation datasets, and 15 general benchmarks, HyperRepair retains 99.5% of fine‑tuned domain performance and 93.4% of the base model’s general performance.