ARIADNE: Agnostic Routing for Inference-time Adapter DyNamic sElection
Quick Answer
ARIADNE introduces a training-free, adapter-agnostic routing framework for dynamic adapter selection at inference time, achieving 97.44% of upper bound performance on Llama 3.2 1B Instruct across 23 NLP tasks.
Quick Take
It scales to 44 tasks with 89.7% average selection accuracy without requiring additional training or access to adapter internals.
Key Points
- ARIADNE uses centroids from training set embeddings for adapter selection.
- Compatible with any parameter-efficient fine-tuning methods without modifications.
- Achieves 97.44% performance on Llama 3.2 1B Instruct across 23 tasks.
- Scales to 44 tasks with an average selection accuracy of 89.7%.
- Eliminates the need for additional router training or internal adapter access.
Paper Resources
Source Excerpt
The increasing deployment of parameter-efficient fine-tuning (PEFT) has led to model ecosystems in which a single backbone is paired with many task-specialized adapters. In this setting, inference-time queries often arrive without task labels, requiring the system to automatically select the most appropriate adapter from a growing and heterogeneous adapter pool. Existing routing methods either depend on access to adapter internals, such as weight decompositions or gradient-based statistics, or r
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