Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning

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.

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Overview

Challenge

Approach

Outcome

Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs (2024.findings-acl)

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Thi Nguyen, Linhao Luo, Fatemeh Shiri, Dinh Phung, Yuan-Fang Li, Thuy-Trang Vu, Gholamreza Haffari

Structured Data in NLP

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Ori Yoran, Tomer Wolfson, Ben Bogin, Uri Katz, Daniel Deutch, Jonathan Berant

Question Answering

Challenge:	Modern systems for multi-hop question answering (QA) break questions into a sequence of reasoning steps, termed chain-of-thought (CoT) Often, multiple chains are sampled and aggregated, but the intermediate steps themselves are discarded.
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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

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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.
Approach:	They propose a chain-guided retriever-reader framework that performs explainable reasoning without corpus annotations.
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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.
Approach:	They propose a framework for integrating text-based and triple-based paradigms that incorporates structured knowledge into large-scale question answering.
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Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics (2025.findings-emnlp)

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Sheldon Yu, Yuxin Xiong, Junda Wu, Xintong Li, Tong Yu, Xiang Chen, Ritwik Sinha, Jingbo Shang, Julian McAuley

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Challenge:	Recent advances in chain-of-thought prompting have demonstrated the ability of large language models to perform multi-step reasoning.
Approach:	They propose a framework to analyze latent dynamics of CoT trajectories for interpretability . they segment generated CoT into discrete reasoning steps and abstract each step into a spectral embedding based on token-level Gram matrices .
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HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering (2025.findings-emnlp)

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Runsong Jia, Mengjia Wu, Ying Ding, Jie Lu, Yi Zhang

Question Answering

Challenge:	graph neural networks capture structured graph information, but lack integration at the reasoning level.
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WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference (2020.lrec-1)

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Zhengnan Xie, Sebastian Thiem, Jaycie Martin, Elizabeth Wainwright, Steven Marmorstein, Peter Jansen

Explainability & Interpretability in NLP

Challenge:	Standardized science questions require combining an average of 6 facts and as many as 16 facts to answer and explain.
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Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering (2025.findings-acl)

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Binquan Ji, Haibo Luo, YifeiLu YifeiLu, Lei Hei, Jiaqi Wang, Tingjing Liao, Wang Lingyu, Shichao Wang, Feiliang Ren

Natural Language Interfaces

Challenge:	Existing approaches to solve multi-hop question answering challenges require multiple rounds of retrieval and iterative generation.
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Exploring Layer Activation Dynamic of CoT via Knowledge Probe (2026.acl-long)

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Chuanxin Zhang, Jiajun Liu, Yao He, Wenjun Ke, Peng Wang, Yankun Le, Sirui Liu, Zhaoyu Yang

Information Extraction & Text Mining

Challenge:	Chain-of-thought reasoning has emerged as a crucial paradigm for multi-step reasoning tasks.
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