Multi-task instruction fine-tuning of a 305M-parameter retriever on enterprise workflow data yields out-of-domain and multilingual recall gains over BM25 and larger embedding models.
Multi-task Retrieval for Knowledge-Intensive Tasks
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abstract
Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking. Although neural retrieval outperforms traditional methods like tf-idf and BM25, its performance degrades considerably when applied to out-of-domain data. Driven by the question of whether a neural retrieval model can be universal and perform robustly on a wide variety of problems, we propose a multi-task trained model. Our approach not only outperforms previous methods in the few-shot setting, but also rivals specialised neural retrievers, even when in-domain training data is abundant. With the help of our retriever, we improve existing models for downstream tasks and closely match or improve the state of the art on multiple benchmarks.
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Multi-task retriever fine-tuning for domain-specific and efficient RAG
Multi-task instruction fine-tuning of a 305M-parameter retriever on enterprise workflow data yields out-of-domain and multilingual recall gains over BM25 and larger embedding models.