Before revision: 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.
After revision: Multi-hop reasoning (i.e., reasoning across two or more documents) at scale is a key step toward 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 robustness and scalability of multi-hop reasoning over current approaches.