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Multi-hop reasoning (i.e., reasoning across two or more documents) is a keyingredient for NLP models that leverage large corpora to exhibit broadknowledge. To retrieve evidence passages, multi-hop models must contend with afast-growing search space across the hops, represent complex queries thatcombine multiple information needs, and resolve ambiguity about the best orderin which to hop between training passages. We tackle these problems via Baleen,a system that improves the accuracy of multi-hop retrieval while learningrobustly from weak training signals in the many-hop setting. To tame the searchspace, we propose condensed retrieval, a pipeline that summarizes the retrievedpassages after each hop into a single compact context. To model complexqueries, we introduce a focused late interaction retriever that allowsdifferent parts of the same query representation to match disparate relevantpassages. Lastly, to infer the hopping dependencies among unordered trainingpassages, we devise latent hop ordering, a weak-supervision strategy in whichthe trained retriever itself selects the sequence of hops. We evaluate Baleenon retrieval for two-hop question answering and many-hop claim verification,establishing state-of-the-art performance.

Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval