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Found 3 papers, 2 papers with code
Towards Modular LLMs by Building and Reusing a Library of LoRAs
The growing number of parameter-efficient adaptations of a base large language model (LLM) calls for studying whether we can reuse such trained adapters to improve performance for new tasks.
Joint Prompt Optimization of Stacked LLMs using Variational Inference
Thus, they can be seen as stochastic language layers in a language network, where the learnable parameters are the natural language prompts at each layer.
Multi-Head Adapter Routing for Cross-Task Generalization
We find that routing is most beneficial during multi-task pre-training rather than during few-shot adaptation and propose $\texttt{MHR}$-$\mu$, which discards routing and fine-tunes the average of the pre-trained adapters on each downstream tasks.
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