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.
Outcome: The proposed framework improves multi-hop question answering by incorporating structured knowledge into the models.

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HOLMES: Hyper-Relational Knowledge Graphs for Multi-hop Question Answering using LLMs (2024.acl-long)

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Challenge: Existing approaches to answer multi-hop questions are query-agnostic and the extracted facts are ambiguous as they lack context.
Approach: They propose to use a knowledge graph to extract query-relevant information from unstructured text.
Outcome: The proposed method achieves performance improvements on two popular datasets.
Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning (2023.findings-emnlp)

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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.
Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering (2025.findings-acl)

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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.
LLM-Based Multi-Hop Question Answering with Knowledge Graph Integration in Evolving Environments (2024.findings-emnlp)

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Challenge: Existing methods for knowledge editing in Large Language Models face difficulties with multi-hop questions that require accurate fact identification and sequential logical reasoning.
Approach: They propose a method that merges explicit knowledge representations of Knowledge Graphs with the linguistic flexibility of Large Language Models to convert free-form language into structured queries and fact triples.
Outcome: The proposed method significantly surpasses state-of-the-art knowledge editing methods in the multi-hop question answering benchmark, MQuAKE.
PokeMQA: Programmable knowledge editing for Multi-hop Question Answering (2024.acl-long)

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Challenge: Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine’s comprehension and reasoning abilities, where large language models (LLMs) have widely achieved the human-comparable performance.
Approach: They propose a framework to edit multi-hop question models to update model with up-to-date facts while avoiding expensive re-training or fine-tuning.
Outcome: The proposed framework outperforms all competitors in multi-hop question answering tasks and consistently produces reliable reasoning process.
From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs (2026.findings-acl)

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Challenge: Large Language Models (LLMs) exhibit limitations in complex multi-hop question answering tasks that necessitate non-linear, structured reasoning.
Approach: They propose an ontology-driven reasoning and chain framework that combines LLMs’ generative capabilities with the structural benefits of knowledge graphs.
Outcome: Extensive experiments across a diverse set of models and standard MQA benchmarks demonstrate that the proposed framework achieves competitive performance while producing more interpretable reasoning chains.
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)

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Challenge: Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources.
Approach: They propose a model that uses symbolic language to generate symbolic queries . they use a dataset that is generated using predefined reasoning chains and human annotation .
Outcome: The proposed model outperforms previous approaches by a significant margin in QA tasks over text.
Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs? (2026.eacl-long)

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Challenge: Existing literature on knowledge extraction for question answering questions whether it is still relevant for question answerrs.
Approach: They extend an existing benchmark with knowledge extraction annotations and evaluate commercial and open-source LLMs of varying sizes.
Outcome: The proposed model can achieve high QA accuracy, but can still benefit from knowledge extraction through augmentation with extracted triples and multi-task learning.
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning (2024.naacl-long)

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Challenge: Existing prompting methods rely on only one or two of these sources, or require repeatedly invoking large language models to generate similar or identical content.
Approach: They propose a semi-structured prompting approach that integrates parametric memory with unstructured knowledge from text documents and structured knowledge from knowledge graphs.
Outcome: The proposed prompting method surpasses existing prompting methods even exceeding those that require fine-tuning on open-domain multi-hop question answering datasets.
Triggering Multi-Hop Reasoning for Question Answering in Language Models using Soft Prompts and Random Walks (2023.findings-acl)

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Challenge: Existing methods that decompose multi-hop questions into single hop sub-questions are difficult to implement.
Approach: They propose to use random-walks to guide pre-trained language models to map multi-hop questions to random-walked paths that lead to the answer.
Outcome: The proposed methods improve on two T5 LMs.

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