Knowledge-Intensive QA
QA: Question Answering
Overview
Knowledge-intensive question answering studies how language models can find and use information beyond what they reliably recall from their parameters, including across documents, tables, knowledge graphs, and changing sources. Common approaches include sparse, dense, and hybrid retrieval-augmented generation; hierarchical or graph-based knowledge organization; and agents that search, inspect evidence, and use tools over multiple steps. Training methods also aim to improve parametric knowledge access or teach models when to verify, revise, or abstain, with evaluation spanning open-domain benchmarks such as TriviaQA and HotpotQA as well as long-document and enterprise tasks. A central challenge is turning relevant retrieved material into complete, well-supported answers at manageable cost, while reliably detecting missing or conflicting evidence.
Latest papers
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