Papers with multi-hop QA datasets
Don’t Forget the Base Retriever! A Low-Resource Graph-based Retriever for Multi-hop Question Answering (2025.emnlp-industry)
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Andre Melo, Enting Chen, Pavlos Vougiouklis, Chenxin Diao, Shriram Piramanayagam, Ruofei Lai, Jeff Z. Pan
| Challenge: | Existing GraphRAG approaches to multi-hop question answering rely on expensive LLM calls. |
| Approach: | They propose a lightweight, low-resource, multi-step graph-based retriever for multi-hop QA that performs multi- step retrieval in a few hundred milliseconds. |
| Outcome: | The proposed retriever outperforms conventional retrievers on multi-hop QA datasets and shows strong potential as a base retriever within multi-step agentic frameworks. |
TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation (2024.findings-emnlp)
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| Challenge: | Existing retrievers are not perfect and often include irrelevant documents in the retrieved set. |
| Approach: | They propose to construct knowledge-grounded reasoning chains from retrieved documents to integrate supporting evidence into RAG models. |
| Outcome: | The proposed model achieves an average performance improvement of 14.03% on three multi-hop QA datasets. |
KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation (2025.acl-long)
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| Challenge: | Iterative retrieval-augmented generation models are difficult to use for multihop question answering (QA) . their retrieval processes can be disrupted by irrelevant documents or factually inaccurate chain-of-thoughts . |
| Approach: | They propose a knowledge-driven iterative retriever model that decomposes documents into knowledge triples and performs iterativ retrieval with these triples to enable a factually reliable retrieval process. |
| Outcome: | The proposed model outperforms existing iRAG models with an average improvement of 9.40% in R@3 and 5.14% in F1 on multi-hop QA datasets. |
Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering (2025.acl-long)
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| 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. |
| Outcome: | The proposed method outperforms baselines on three multi-hop QA datasets. |