LoRA on the Go: Training-Free, On-the-Fly Adapter Mixing for LLMs
TL;DR
LoGo lets large language models pick and blend the right LoRA adapters for each input—no extra training, labels, or task setup.
- Training-free: uses signals from a single forward pass to select and weight adapters.
- Instance-level: decisions happen on-the-fly for every query.
- Practical: keeps inference throughput while handling diverse, unpredictable domains.
- Effective: across 5 NLP benchmarks, 27 datasets, 3 model families; up to 3.6% gains over training-based baselines, competitive elsewhere.
Why it matters: Real-world inputs aren’t neatly labeled. Instead of betting on one task-specific LoRA, LoGo dynamically assembles the best mix for whatever comes its way.
Authors: Seungeon Lee, Soumi Das, Manish Gupta, Krishna P. Gummadi. Paper: http://arxiv.org/abs/2511.07129v1
Paper: http://arxiv.org/abs/2511.07129v1
Register: https://www.AiFeta.com
#LLM #LoRA #NLP #AI #MachineLearning #Adapters #Inference #Research
