# Multi-hop Question Answering

arXiv: https://arxiv.org/abs/2204.09140 · HTML: https://mlsanity.com/paper/2204.09140/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2022.

## Built on by (most cited first, 30 of 64)

- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models](https://mlsanity.com/paper/2404.18796/) (2024)
- [Query Rewriting for Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2305.14283/) (2023)
- [Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering](https://mlsanity.com/paper/2306.04136/) (2023)
- [Why Does ChatGPT Fall Short in Providing Truthful Answers?](https://mlsanity.com/paper/2304.10513/) (2023)
- [Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach](https://mlsanity.com/paper/2407.13101/) (2024)
- [Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models](https://mlsanity.com/paper/2406.17169/) (2024)
- [Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models](https://mlsanity.com/paper/2310.05253/) (2023)
- [WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models](https://mlsanity.com/paper/2311.07138/) (2023)
- [TurkishMMLU: Measuring Massive Multitask Language Understanding in Turkish](https://mlsanity.com/paper/2407.12402/) (2024)
- [Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning](https://mlsanity.com/paper/2310.13552/) (2023)
- [REAPER: Reasoning based Retrieval Planning for Complex RAG Systems](https://mlsanity.com/paper/2407.18553/) (2024)
- [Lost in the Middle, and In-Between: Enhancing Language Models' Ability to Reason Over Long Contexts in Multi-Hop QA](https://mlsanity.com/paper/2412.10079/) (2024)
- [Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top](https://mlsanity.com/paper/2405.15452/) (2024)
- [Credible Plan-Driven RAG Method for Multi-Hop Question Answering](https://mlsanity.com/paper/2504.16787/) (2025)
- [OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering](https://mlsanity.com/paper/2409.08250/) (2024)
- [DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent](https://mlsanity.com/paper/2603.01152/) (2026)
- [A Fano-Style Accuracy Upper Bound for LLM Single-Pass Reasoning in Multi-Hop QA](https://mlsanity.com/paper/2509.21199/) (2025)
- [RAG-based Question Answering over Heterogeneous Data and Text](https://mlsanity.com/paper/2412.07420/) (2024)
- [GRACE: Reinforcement Learning for Grounded Response and Abstention under Contextual Evidence](https://mlsanity.com/paper/2601.04525/) (2026)
- [EpiK-Eval: Evaluation for Language Models as Epistemic Models](https://mlsanity.com/paper/2310.15372/) (2023)
- [Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning](https://mlsanity.com/paper/2310.16731/) (2023)
- [Self-Evaluating LLMs for Multi-Step Tasks: Stepwise Confidence Estimation for Failure Detection](https://mlsanity.com/paper/2511.07364/) (2025)
- [Wrong Answers Can Also Be Useful: PlausibleQA -- A Large-Scale QA Dataset with Answer Plausibility Scores](https://mlsanity.com/paper/2502.16358/) (2025)
- [On Generalization across Measurement Systems: LLMs Entail More Test-Time Compute for Underrepresented Cultures](https://mlsanity.com/paper/2506.02591/) (2025)
- [Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection](https://mlsanity.com/paper/2507.18202/) (2025)
- [Who's Asking? Evaluating LLM Robustness to Inquiry Personas in Factual Question Answering](https://mlsanity.com/paper/2510.12925/) (2025)
- [Rethinking Label Smoothing on Multi-hop Question Answering](https://mlsanity.com/paper/2212.09512/) (2022)
- [Performance Prediction for Multi-hop Questions](https://mlsanity.com/paper/2308.06431/) (2023)
- [Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering](https://mlsanity.com/paper/2403.12393/) (2024)

## Abstract (arXiv)

The task of Question Answering (QA) has attracted significant research interest for long. Its relevance to language understanding and knowledge retrieval tasks, along with the simple setting makes the task of QA crucial for strong AI systems. Recent success on simple QA tasks has shifted the focus to more complex settings. Among these, Multi-Hop QA (MHQA) is one of the most researched tasks over the recent years. In broad terms, MHQA is the task of answering natural language questions that involve extracting and combining multiple pieces of information and doing multiple steps of reasoning. An example of a multi-hop question would be "The Argentine PGA Championship record holder has won how many tournaments worldwide?". Answering the question would need two pieces of information: "Who is the record holder for Argentine PGA Championship tournaments?" and "How many tournaments did [Answer of Sub Q1] win?". The ability to answer multi-hop questions and perform multi step reasoning can significantly improve the utility of NLP systems. Consequently, the field has seen a surge with high quality datasets, models and evaluation strategies. The notion of 'multiple hops' is somewhat abstract which results in a large variety of tasks that require multi-hop reasoning. This leads to different datasets and models that differ significantly from each other and makes the field challenging to generalize and survey. We aim to provide a general and formal definition of the MHQA task, and organize and summarize existing MHQA frameworks. We also outline some best practices for building MHQA datasets. This book provides a systematic and thorough introduction as well as the structuring of the existing attempts to this highly interesting, yet quite challenging task.