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

Source: [Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions?](<https://www.sota2.com/research/paper/68fa69321e209131cea20bdd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:55:51.498Z

Authors: Yixuan Tang, Hwee Tou Ng, Anthony K.H. Tung

Published: 2020-02-23T15:16:43.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2002.09919>)

[PDF](<https://arxiv.org/pdf/2002.09919.pdf>)