LoRA-Augmented Generation (LAG) for Knowledge-Intensive Language Tasks

William Fleshman; Benjamin Van Durme

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arXiv · 2507.05346

LoRA-Augmented Generation (LAG) for Knowledge-Intensive Language Tasks

William Fleshman·Benjamin Van Durme

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Abstract

The proliferation of fine-tuned language model experts for specific tasks and domains signals the need for efficient selection and combination methods. We propose LoRA-Augmented Generation (LAG) for leveraging large libraries of knowledge and task-specific LoRA adapters. LAG requires no additional training or access to data, and efficiently filters, retrieves, and applies experts on a per-token and layer basis. We evaluate LAG on various knowledge-intensive tasks, achieving superior performance over existing data-free methods. We explore scenarios where additional data is available, demonstrating LAG's compatibility with alternative solutions such as retrieval-augmented generation (RAG).

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William Fleshman, Benjamin Van Durme. 2025-07-07. LoRA-Augmented Generation (LAG) for Knowledge-Intensive Language Tasks. https://arxiv.org/abs/2507.05346

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