In addressing the limitations of large language models (LLMs) when capturing less common knowledge and the high computational costs of extensive pre-training, Researchers from Meta introduce Retrieval-Augmented Dual Instruction Tuning (RA-DIT). RA-DIT is a lightweight fine-tuning methodology designed to equip any LLM with efficient retrieval capabilities. It operates through two distinct fine-tuning stages, each delivering substantial performance enhancements. By optimizing the LM's use of retrieved information and the retriever's content relevance, RA-DIT offers a promising solution to enhance LLMs with retrieval capabilities. RA-DIT provides a lightweight, two-stage fine-tuning method for enhancing LLMs with retrieval capabilities. It optimizes LLMs to use
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AI Transformations
