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 knowledge by 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 retrieval pipeline 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.

中文翻译：

---

Baleen：通过压缩检索进行大规模鲁棒的多跳推理

大规模的多跳推理（即，对两个或多个文档进行推理）是迈向NLP模型的关键一步，该模型可通过利用大量文档来展现广泛的世界知识。我们提出Baleen，它是一种比当前方法提高多跳推理的鲁棒性和可伸缩性的系统。Baleen引入了按跳的精简检索管道来减轻搜索空间的大小，引入可以对复杂的多跳查询进行建模的集中式后期交互检索器（FliBERT），以及一种弱监督策略（潜跳排序），以从有限的信号中学习有关要查询的文档的信息。我们在新的多跳索赔验证数据集HoVer上对Baleen进行评估，从而建立了最先进的性能。