# Unsupervised Multi-hop Question Answering by Question Generation

arXiv: https://arxiv.org/abs/2010.12623 · HTML: https://mlsanity.com/paper/2010.12623/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 30 of 38)

- [MuSiQue: Multihop Questions via Single-hop Question Composition](https://mlsanity.com/paper/2108.00573/) (2021)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Natural Language Reasoning, A Survey](https://mlsanity.com/paper/2303.14725/) (2023)
- [Automatic Generation of Socratic Subquestions for Teaching Math Word Problems](https://mlsanity.com/paper/2211.12835/) (2022)
- [ReasonBERT: Pre-trained to Reason with Distant Supervision](https://mlsanity.com/paper/2109.04912/) (2021)
- [Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering](https://mlsanity.com/paper/2204.04581/) (2022)
- [Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA](https://mlsanity.com/paper/2210.05197/) (2022)
- [S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering](https://mlsanity.com/paper/2305.11725/) (2023)
- [Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering](https://mlsanity.com/paper/2206.08486/) (2022)
- [FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation](https://mlsanity.com/paper/2309.05007/) (2023)
- [MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering](https://mlsanity.com/paper/2310.05007/) (2023)
- [MuGER2: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering](https://mlsanity.com/paper/2210.10350/) (2022)
- [Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering](https://mlsanity.com/paper/2305.13691/) (2023)
- [Are Small Language Models Ready to Compete with Large Language Models for Practical Applications?](https://mlsanity.com/paper/2406.11402/) (2024)
- [Exploring Hybrid Question Answering via Program-based Prompting](https://mlsanity.com/paper/2402.10812/) (2024)
- [Synthetic Multimodal Question Generation](https://mlsanity.com/paper/2407.02233/) (2024)
- [Relation-Guided Pre-Training for Open-Domain Question Answering](https://mlsanity.com/paper/2109.10346/) (2021)
- [Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts](https://mlsanity.com/paper/2205.12496/) (2022)
- [Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labeling](https://mlsanity.com/paper/2404.00571/) (2024)
- [Hybrid Graphs for Table-and-Text based Question Answering using LLMs](https://mlsanity.com/paper/2501.17767/) (2025)
- [QA Is the New KR: Question-Answer Pairs as Knowledge Bases](https://mlsanity.com/paper/2207.00630/) (2022)
- [Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering](https://mlsanity.com/paper/2208.10297/) (2022)
- [CONSISTENT: Open-Ended Question Generation From News Articles](https://mlsanity.com/paper/2210.11536/) (2022)
- [Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Framework](https://mlsanity.com/paper/2212.10097/) (2022)
- [Pre-training Language Models for Comparative Reasoning](https://mlsanity.com/paper/2305.14457/) (2023)
- [A Survey on Neural Question Generation: Methods, Applications, and Prospects](https://mlsanity.com/paper/2402.18267/) (2024)
- [Towards Zero-Shot and Few-Shot Table Question Answering using GPT-3](https://mlsanity.com/paper/2210.17284/) (2022)
- [LogicNet: A Logical Consistency Embedded Face Attribute Learning Network](https://mlsanity.com/paper/2311.11208/) (2023)
- [Weakly Supervised Pre-Training for Multi-Hop Retriever](https://mlsanity.com/paper/2106.09983/) (2021)
- [Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision](https://mlsanity.com/paper/2107.08582/) (2021)

## Abstract (arXiv)

Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. We explore the possibility to train a well-performed multi-hop QA model without referencing any human-labeled multi-hop question-answer pairs, i.e., unsupervised multi-hop QA. We propose MQA-QG, an unsupervised framework that can generate human-like multi-hop training data from both homogeneous and heterogeneous data sources. MQA-QG generates questions by first selecting/generating relevant information from each data source and then integrating the multiple information to form a multi-hop question. Using only generated training data, we can train a competent multi-hop QA which achieves 61% and 83% of the supervised learning performance for the HybridQA and the HotpotQA dataset, respectively. We also show that pretraining the QA system with the generated data would greatly reduce the demand for human-annotated training data. Our codes are publicly available at https://github.com/teacherpeterpan/Unsupervised-Multi-hop-QA.