Exploiting Intrinsic Duality for Multi-Hop Question Generation

• URL: http://arxiv.org/abs/2608.00712v1
• Date: Sat, 01 Aug 2026 15:20:16 GMT
• Title: Exploiting Intrinsic Duality for Multi-Hop Question Generation
• Abstract summary: Multi hop question generation (MQG) aims to generate questions from multiple given documents and target answers, whereas question answering (QA) focuses on deriving answers from documents given specific questions.<n>We propose QQ, a novel framework that exploits the duality between Question and answer for multi hop Question generation.<n>QQ employs a unified architecture functioning simultaneously as both an MQG and a QA model to fully leverage their interdependence.
• Score: 9.34607982361678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi hop question generation (MQG) aims to generate questions from multiple given documents and target answers, whereas question answering (QA) focuses on deriving answers from documents given specific questions. Although MQG and QA are inherently dual tasks, most existing MQG studies largely overlook this intrinsic duality. To address this limitation, we propose QQ, a novel framework that exploits the duality between Question and answer for multi hop Question generation. Specifically, QQ employs a unified architecture functioning simultaneously as both an MQG and a QA model to fully leverage their interdependence. Our framework is driven by two key mechanisms: (i) enforcing bidirectional alignment constraints to ensure strict mutual correspondence between the questions generated by the MQG model and the answers produced by the QA model; and (ii) applying contrastive learning to pull paired question answer representations closer while pushing unpaired ones apart, thereby reinforcing this correspondence. Extensive automatic and human evaluations on the HotpotQA and MuSiQue datasets demonstrate that the QQ framework significantly improves the quality of generated multi hop questions.

Related papers

• The benefits of query-based KGQA systems for complex and temporal questions in LLM era [55.20230501807337]
  Large language models excel in question-answering (QA) yet still struggle with multi-hop reasoning and temporal questions.<n> Query-based knowledge graph QA (KGQA) offers a modular alternative by generating executable queries instead of direct answers.<n>We explore multi-stage query-based framework for WikiData QA, proposing multi-stage approach that enhances performance on challenging multi-hop and temporal benchmarks.
  arXiv  Detail & Related papers  (2025-07-16T06:41:03Z)
• GenDec: A robust generative Question-decomposition method for Multi-hop reasoning [32.12904215053187]
  Multi-hop QA involves step-by-step reasoning to answer complex questions. Existing large language models'(LLMs) reasoning ability in multi-hop question answering remains exploration. It is unclear whether LLMs follow a desired reasoning chain to reach the right final answer.
  arXiv  Detail & Related papers  (2024-02-17T02:21:44Z)
• Improving Question Generation with Multi-level Content Planning [70.37285816596527]
  This paper addresses the problem of generating questions from a given context and an answer, specifically focusing on questions that require multi-hop reasoning across an extended context. We propose MultiFactor, a novel QG framework based on multi-level content planning. Specifically, MultiFactor includes two components: FA-model, which simultaneously selects key phrases and generates full answers, and Q-model which takes the generated full answer as an additional input to generate questions.
  arXiv  Detail & Related papers  (2023-10-20T13:57:01Z)
• RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering [87.18962441714976]
  We introduce RoMQA, the first benchmark for robust, multi-evidence, multi-answer question answering (QA) We evaluate state-of-the-art large language models in zero-shot, few-shot, and fine-tuning settings, and find that RoMQA is challenging. Our results show that RoMQA is a challenging benchmark for large language models, and provides a quantifiable test to build more robust QA methods.
  arXiv  Detail & Related papers  (2022-10-25T21:39:36Z)
• Ask to Understand: Question Generation for Multi-hop Question Answering [11.626390908264872]
  Multi-hop Question Answering (QA) requires the machine to answer complex questions by finding scattering clues and reasoning from multiple documents. We propose a novel method to complete multi-hop QA from the perspective of Question Generation (QG)
  arXiv  Detail & Related papers  (2022-03-17T04:02:29Z)
• QA4QG: Using Question Answering to Constrain Multi-Hop Question Generation [54.136509061542775]
  Multi-hop question generation (MQG) aims to generate complex questions which require reasoning over multiple pieces of information of the input passage. We propose a novel framework, QA4QG, a QA-augmented BART-based framework for MQG. Our results on the HotpotQA dataset show that QA4QG outperforms all state-of-the-art models.
  arXiv  Detail & Related papers  (2022-02-14T08:16:47Z)
• How to Build Robust FAQ Chatbot with Controllable Question Generator? [5.680871239968297]
  We propose a high-quality, diverse, controllable method to generate adversarial samples with a semantic graph. The fluent and semantically generated QA pairs fool our passage retrieval model successfully. We find that the generated data set improves the generalizability of the QA model to the new target domain.
  arXiv  Detail & Related papers  (2021-11-18T12:54:07Z)
• Reinforced Multi-task Approach for Multi-hop Question Generation [47.15108724294234]
  We take up Multi-hop question generation, which aims at generating relevant questions based on supporting facts in the context. We employ multitask learning with the auxiliary task of answer-aware supporting fact prediction to guide the question generator. We demonstrate the effectiveness of our approach through experiments on the multi-hop question answering dataset, HotPotQA.
  arXiv  Detail & Related papers  (2020-04-05T10:16:59Z)
• Unsupervised Question Decomposition for Question Answering [102.56966847404287]
  We propose an algorithm for One-to-N Unsupervised Sequence Sequence (ONUS) that learns to map one hard, multi-hop question to many simpler, single-hop sub-questions. We show large QA improvements on HotpotQA over a strong baseline on the original, out-of-domain, and multi-hop dev sets.
  arXiv  Detail & Related papers  (2020-02-22T19:40:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.