Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering

Challenge:	Existing approaches to solve multi-hop question answering challenges require multiple rounds of retrieval and iterative generation.
Approach:	They propose a framework that decomposes complex questions into coherent subquestions . it then iteratively refines these subquests through context-aware rewriting to generate effective query formulations.
Outcome:	The proposed framework performs on par with or surpasses state-of-the-art benchmarks while significantly reducing token consumption.

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Overview

Challenge

Approach

Outcome

Following Occam’s Razor: Dynamic Combination of Structured Knowledge for Multi-Hop Question Answering using LLMs (2025.findings-emnlp)

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Wei Chen, Zhi Zheng, Lili Zhao, Huijun Hou, Tong Xu

Multimodality

Challenge:	Multi-hop question answering is a challenging task that requires capturing information from multiple positions in multiple documents.
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Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning (2023.findings-emnlp)

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Ruosen Li, Xinya Du

Responsible & Trustworthy NLP

Challenge:	Neural models, including large language models (LLMs), achieve superior performance on multi-hop question-answering tasks.
Approach:	They propose to use the chain-of-thought mechanism to generate both the reasoning chain and the answer.
Outcome:	Empirical results show that the proposed framework generates more faithful reasoning chains and significantly improves the QA performance on two benchmark datasets.

Robust Knowledge Editing via Explicit Reasoning Chains for Distractor-Resilient Multi-Hop QA (2025.findings-emnlp)

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Yuchen Wu, Liang Ding, Li Shen, Dacheng Tao

Robustness in NLP

Challenge:	Large language models encode vast amounts of knowledge but remain static once trained, making timely integration of emerging facts prohibitively expensive via full retraining.
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Refiner: Restructure Retrieved Content Efficiently to Advance Question-Answering Capabilities (2024.findings-emnlp)

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Zhonghao Li, Xuming Hu, Aiwei Liu, Kening Zheng, Sirui Huang, Hui Xiong

Green & Sustainable NLP

Challenge:	Large Language Models (LLMs) are limited by their parametric knowledge, leading to hallucinations in knowledge-extensive tasks.
Approach:	They propose an end-to-end extract-and-restructure paradigm that leverages a single decoder-only LLM to adaptively extract query-relevant contents verbatim along with the necessary context.
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LOG: A Local-to-Global Optimization Approach for Retrieval-based Explainable Multi-Hop Question Answering (2025.coling-main)

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Hao Xu, Yunxiao Zhao, Jiayang Zhang, Zhiqiang Wang, Ru Li

Responsible & Trustworthy NLP

Challenge:	Existing approaches to multi-hop question answering emphasize single-step and multi-step iterative decomposition or retrieval, which are susceptible to failure in long-chain reasoning due to the progressive accumulation of erroneous information.
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From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs (2026.findings-acl)

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Haonan Bian, Yutao Qi, Rui Yang, Yuanxi Che, Jiaqian Wang, Heming Xia, Ranran Zhen

Semantic Text Processing

Challenge:	Large Language Models (LLMs) exhibit limitations in complex multi-hop question answering tasks that necessitate non-linear, structured reasoning.
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HOLMES: Hyper-Relational Knowledge Graphs for Multi-hop Question Answering using LLMs (2024.acl-long)

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Pranoy Panda, Ankush Agarwal, Chaitanya Devaguptapu, Manohar Kaul, Prathosh Ap

Semantic Text Processing

Challenge:	Existing approaches to answer multi-hop questions are query-agnostic and the extracted facts are ambiguous as they lack context.
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Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering (2025.acl-long)

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Rongzhi Zhu, Xiangyu Liu, Zequn Sun, Yiwei Wang, Wei Hu

Natural Language Interfaces

Challenge:	Empirical results show that ChainRAG consistently outperforms baselines in both effectiveness and efficiency.
Approach:	They propose a method which sequentially handles each sub-question by completing missing key entities and retrieving relevant sentences from a sentence graph for answer generation.
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Exploiting Reasoning Chains for Multi-hop Science Question Answering (2021.findings-emnlp)

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Weiwen Xu, Yang Deng, Huihui Zhang, Deng Cai, Wai Lam

Responsible & Trustworthy NLP

Challenge:	Existing frameworks for multi-hop Science question answering do not require corpus-specific annotations.
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Commonsense for Generative Multi-Hop Question Answering Tasks (D18-1)

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Lisa Bauer, Yicheng Wang, Mohit Bansal

Reasoning

Challenge:	Reading comprehension QA tasks have seen a recent surge in popularity, yet most work has focused on fact-finding extractive QA.
Approach:	They propose a multi-hop generative task that uses a pointer-generator decoder to synthesize disjoint pieces of information within the context to generate an answer.
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