# Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions?

arXiv: https://arxiv.org/abs/2002.09919 · HTML: https://mlsanity.com/paper/2002.09919/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2020.

## Built on by (most cited first, 30 of 30)

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- [Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process](https://mlsanity.com/paper/2402.19350/) (2024)
- [Towards Interpretable and Reliable Reading Comprehension: A Pipeline Model with Unanswerability Prediction](https://mlsanity.com/paper/2111.09029/) (2021)
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- [In-Context Ability Transfer for Question Decomposition in Complex QA](https://mlsanity.com/paper/2310.18371/) (2023)
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- [Reasoning Hijacking: The Fragility of Reasoning Alignment in Large Language Models](https://mlsanity.com/paper/2601.10294/) (2026)
- [Debating the Unspoken: Role-Anchored Multi-Agent Reasoning for Half-Truth Detection](https://mlsanity.com/paper/2604.19005/) (2026)
- [Multi-granular Training Strategies for Robust Multi-hop Reasoning Over Noisy and Heterogeneous Knowledge Sources](https://mlsanity.com/paper/2502.05944/) (2025)
- [Evaluating Prompt Engineering Techniques for RAG in Small Language Models: A Multi-Hop QA Approach](https://mlsanity.com/paper/2602.13890/) (2026)
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- [Locating Answer-Correctness Signals in Frozen Large Language Models](https://mlsanity.com/paper/2609.37700/) (2026)

## Abstract (arXiv)

Multi-hop question answering (QA) requires a model to retrieve and integrate information from different parts of a long text to answer a question. Humans answer this kind of complex questions via a divide-and-conquer approach. In this paper, we investigate whether top-performing models for multi-hop questions understand the underlying sub-questions like humans. We adopt a neural decomposition model to generate sub-questions for a multi-hop complex question, followed by extracting the corresponding sub-answers. We show that multiple state-of-the-art multi-hop QA models fail to correctly answer a large portion of sub-questions, although their corresponding multi-hop questions are correctly answered. This indicates that these models manage to answer the multi-hop questions using some partial clues, instead of truly understanding the reasoning paths. We also propose a new model which significantly improves the performance on answering the sub-questions. Our work takes a step forward towards building a more explainable multi-hop QA system.