Papers with multi-hop QA task
Time Sensitive Knowledge Editing through Efficient Finetuning (2024.acl-short)
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Xiou Ge, Ali Mousavi, Edouard Grave, Armand Joulin, Kun Qian, Benjamin Han, Mostafa Arefiyan, Yunyao Li
| Challenge: | Existing locate-and-edit knowledge editing methods suffer from two limitations: they are infeasible for large scale KE in practice and require long run-time. |
| Approach: | They propose to use parametric fine-tuning techniques to update obsolete knowledge and induce new knowledge into LLMs. |
| Outcome: | The proposed methods improve the performance of KE and knowledge update in a temporal dataset with knowledge update and knowledge injection examples. |
Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering (2023.findings-eacl)
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| Challenge: | Existing studies have utilized underlying reasoning (UR) tasks in multi-hop question answering datasets to explain the predicted answers and evaluate models' reasoning abilities. |
| Approach: | They analyze UR tasks in QA datasets to determine their effectiveness . they find that UR task is helpful in preventing reasoning shortcuts . |
| Outcome: | The proposed model improves QA performance, reasoning shortcuts, and robustness on adversarial questions. |
Prompt-based Conservation Learning for Multi-hop Question Answering (2022.coling-1)
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| Challenge: | Existing multi-hop QA methods fail to answer a large fraction of sub-questions even if their parent questions are answered correctly. |
| Approach: | They propose a Prompt-based Conservation Learning framework that acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop tasks. |
| Outcome: | The proposed framework acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop tasks, mitigating forgetting. |
BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answering (2025.acl-long)
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| Challenge: | Existing studies on multi-hop question answering employ specific methods regardless of question types . complexity of multihop question answerrs often exceeds knowledge boundaries of LLMs . |
| Approach: | They propose a framework that uses chain-of-thought prompting to prompt LLMs to answer multi-hop questions. |
| Outcome: | The proposed framework outperforms baseline models in multi-hop QA scenarios. |
DRAMA: Dynamic Multi-Granularity Graph Estimate Retrieval over Tabular and Textual Question Answering (2024.lrec-main)
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| Challenge: | TableTextQA tasks require tabular and textual data, gaining increasing attention . however, row-based approaches suffer from limitations such as lack of interaction between rows . |
| Approach: | They propose a method that incorporates an interaction mechanism among multiple rows . Empirical results demonstrate that the proposed method is effective . |
| Outcome: | Empirical results show that the proposed model is effective on tabFact and HybridQA datasets. |
Retrieval Heads are Dynamic (2026.acl-long)
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Yuping Lin, Zitao Li, Yue Xing, Pengfei He, Yingqian Cui, Yaliang Li, Bolin Ding, Jingren Zhou, Jiliang Tang
| Challenge: | Recent studies have identified "retrieval heads" in Large Language Models responsible for extracting information from input contexts. |
| Approach: | They propose to examine retrieval heads from a dynamic perspective . they establish that retrieval head activation is highly dynamic and functionally irreplaceable . |
| Outcome: | The proposed model's hidden state encodes a predictive signal for future retrieval head patterns, indicating an internal planning mechanism. |
More Documents, Same Length: Isolating the Challenge of Multiple Documents in RAG (2025.findings-emnlp)
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| Challenge: | Retrieval-Augmented Generation (RAG) enhances the accuracy of Large Language Models by leveraging relevant external documents during generation. |
| Approach: | They evaluate various language models on custom datasets derived from QA tasks . they keep context length and position of relevant information constant while varying the number of documents . |
| Outcome: | The proposed method improves the accuracy of large language models by leveraging external documents . increasing document count reduces performance by up to 20%, the authors find . |