How does the performance of instruction-tuned retrievers on multi-hop queries from MuSiQue compare to single-c

Abstract

Abstract: Instruction-tuned language models (LM) are able to respond to imperative commands, providing a more natural user interface compared to their base counterparts. In this work, we present Promptriever, the first retrieval model able to be prompted like an LM. To train Promptriever, we curate and release a new instance-level instruction training set from MS MARCO, spanning nearly 500k instances. Promptriever not only achieves strong performance on standard retrieval tasks, but also follows instructions. We observe: (1) large gains (reaching SoTA) on following detailed relevance instructions (+14.3

Research Question

How does the performance of instruction-tuned retrievers on multi-hop queries from MuSiQue compare to single-context adversarial training when evaluated on out-of-domain BEIR subsets (SciFact, TREC-COVID) in terms of MRR@10?

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