Diagnosing the Fact-Grounding Gap in Multi-Hop Question Answering

arXiv:2609.17043 · cs.CL, cs.AI, cs.IR · Submitted 2026-09-15 · Read on arXiv

cs.CL, cs.AI, cs.IR

Submitted: 2026-09-15

Updated: 2026-09-15

Comments: Accepted to EMNLP 2026 Main Conference

Code: https://github.com/RichardZhu123/fact-grounding-gap

License: http://creativecommons.org/licenses/by/4.0/

The gist: Multi-hop question answering requires combining information from multiple documents to answer complex questions.

Terminology

Abstract

Multi-hop question answering requires combining information from multiple documents to answer complex questions. These systems have grown increasingly capable, yet when they fail, the error is typically attributed to not finding the right documents. Whether this holds at the level of individual reasoning steps remains largely unexamined. We investigate this across three standard multi-hop QA benchmarks and find that failures decompose into two distinct modes: retrieval failures, where the needed passage was not retrieved, and extraction failures, where the passage was retrieved but the needed fact could not be extracted - a phenomenon we term the fact-grounding gap. Extraction failures account for nearly half of all per-hop deficiencies and are invisible to standard retrieval metrics. They remain unresolved by every retrieval intervention we test, establishing a ceiling for retrieval-only improvements. The gap's severity varies across benchmarks and question types, but extraction failures appear on every dataset we measure. Our findings reveal that retrieval failures and extraction failures are fundamentally different bottlenecks requiring different solutions - a distinction absent from current evaluation practice.

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