AutoAdapt trains independent LoRA adapters via automatic latent domain discovery, matching an all-domain single adapter across 14 benchmarks while enabling retraining-free extension
AutoAdapt is a modular instruction-tuning framework that first discovers latent domains with three methods (K-means, BERTopic, MNLI), then trains one LoRA adapter per domain independently and in parallel, and routes at inference with a parameter-free centroid mechanism; across 14 domain-specific benchmarks and GPT-4o pairwise judgements it reaches parity with a single LoRA adapter trained on all domains, while adding or updating a domain requires training only one adapter instead of full-model retraining.