Multi-hop reasoning (i.e., reasoning across two or more documents) at scale is a key step toward NLP models that can exhibit broad world knowledgeby leveraging large collections of documents. We propose Baleen, a system that improves the robustness and scalability of multi-hop reasoning over current approaches. Baleen introduces a per-hop condensed retrievalpipeline to mitigate the size of the search space, a focused late interaction retriever (FliBERT) that can model complex multi-hop queries, and a weak supervision strategy, latent hop ordering, to learn from limited signal about which documents to retrieve for a query . We evaluate Baleen on the new many-hop claim verification dataset HoVer , establishing state-of-the-art performance.
Multi-hop reasoning (i.e., reasoning across two or more documents) is a key ingredient for NLP models that leverage large corpora to exhibit broad knowledge. To retrieve evidence passages, multi-hop models must contend with a fast-growing search space across the hops, represent complex queries that combine multiple information needs, and resolve ambiguity about the best order in which to hop between training passages. We tackle these problems via Baleen, a system that improves the accuracy and robustness of multi-hop retrieval. To tame the search space, we propose condensed retrieval, a pipeline that summarizes the retrieved passages after each hop into a single compact context. To model complex queries, we introduce a focused late interaction retriever that allows different parts of the same query representation to match disparate relevant passages. Lastly, to infer the hopping dependencies among unordered training passages, we devise latent hop ordering, a weak-supervision strategy in which the trained retriever itself selects the sequence of hops . We evaluate Baleen on retrieval for two-hop question answering and many-hop claim verification , establishing state-of-the-art performance.
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at scale
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step toward
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can exhibit broad world knowledgeby leveraging large collections of documents. We propose
leverage large corpora to exhibit broad knowledge. To retrieve evidence passages, multi-hop models must contend with a fast-growing search space across the hops, represent complex queries that combine multiple information needs, and resolve ambiguity about the best order in which to hop between training passages. We tackle these problems via
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robustness and scalability
accuracy and robustness
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reasoning over current approaches. Baleen introduces a per-hop condensed retrievalpipeline to mitigate the size of the search space,
retrieval. To tame the search space, we propose condensed retrieval, a pipeline that summarizes the retrieved passages after each hop into a single compact context. To model complex queries, we introduce
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(FliBERT) that can model complex multi-hop queries, and a weak supervision strategy,
that allows different parts of the same query representation to match disparate relevant passages. Lastly, to infer the hopping dependencies among unordered training passages, we devise
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to learn from limited signal about which documents to retrieve for a query
a weak-supervision strategy in which the trained retriever itself selects the sequence of hops
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the new
retrieval for two-hop question answering and
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dataset HoVer