| Challenge: | Existing approaches to use entailment models for question answering are limited . large scale datasets are typically framed at a sentence level, whereas question answering requires verifying whether multiple sentences, taken together as a premise, entitle a hypothesis. |
| Approach: | They propose a general architecture that can use entailment models for multi-hop QA tasks. |
| Outcome: | The proposed model outperforms QA models trained on target datasets and the OpenAI transformer models. |
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Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification (2024.findings-acl)
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| Challenge: | Existing evidence that humans make numerous inferences to understand discourse and text is not fully understood. |
| Approach: | They propose to use textual inference datasets with multi-sentence premises to solve the entailment verification problem. |
| Outcome: | The proposed model outperforms GPT-3.5 and rivals GPL-4 in EV tasks. |
End-to-End Beam Retrieval for Multi-Hop Question Answering (2024.naacl-long)
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| Challenge: | Existing beam retrieval frameworks for multi-hop question answering were customized for two-hop questions and were poorly supervised. |
| Approach: | They propose an end-to-end beam retrieval framework for multi-hop question answering . they combine an encoder and two classification heads to optimize the retrieval process . |
| Outcome: | The proposed framework improves on MuSiQue-Ans and surpasses all previous retrievers on HotpotQA and achieves 99.9% precision on 2WikiMultiHopQA. |
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. |
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering (D18-1)
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Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, Christopher D. Manning
| Challenge: | Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. |
| Approach: | They propose a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) the questions provide sentence-level supporting facts required for reasoning; and (4) a type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison. |
| Outcome: | The proposed dataset has 113k Wikipedia-based question-answer pairs and four key features that make it challenging for the latest QA systems. |
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps (2020.coling-main)
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| Challenge: | Existing multi-hop question answering datasets do not provide a complete explanation for the reasoning process from the question to the answer. |
| Approach: | They propose a multi-hop question answering dataset that uses structured and unstructured data to test reasoning skills. |
| Outcome: | The proposed dataset ensures multi-hop reasoning while being challenging for multi-models. |
Unsupervised Multi-hop Question Answering by Question Generation (2021.naacl-main)
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| Challenge: | Existing training data for multi-hop question answering (QA) is time-consuming and resource-intensive. |
| Approach: | They propose an unsupervised framework that generates human-like multi-hop training data from homogeneous and heterogeneously data sources. |
| Outcome: | The proposed framework achieves 61% and 83% of the supervised learning performance for the HybridQA and HotpotQA datasets. |
Weakly Supervised Pre-Training for Multi-Hop Retriever (2021.findings-acl)
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| Challenge: | Existing methods for weakly supervised multi-hop pretraining require costly human annotation. |
| Approach: | They propose a method for weakly supervised multi-hop retriever pretraining without human efforts by generating vector representations of complex questions and subquestion as weak supervision for pre-training. |
| Outcome: | The proposed method is effective and robust on limited data and computational resources. |
Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions? (2021.eacl-main)
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| Challenge: | Existing models fail to answer a large portion of sub-questions . Existing systems have achieved super-human performance . |
| Approach: | They propose to use a neural decomposition model to generate sub-questions for a multi-hop question and extract the corresponding sub-answers. |
| Outcome: | The proposed model is based on a hotpotQA dataset with a multi-hop question and sub-answers. |
Reinforced Multi-task Approach for Multi-hop Question Generation (2020.coling-main)
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| Challenge: | Empirical evaluation shows our model to outperform the single-hop question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE and human evaluation metrics for quality and coverage of the generated questions. |
| Approach: | They propose a question-aware reward function to maximize the utilization of supporting facts in the context. |
| Outcome: | The proposed model outperforms single-hop neural question generation models on automatic evaluation metrics and human evaluation metrics for quality and coverage of the generated questions. |
Understanding Dataset Design Choices for Multi-hop Reasoning (N19-1)
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| Challenge: | Existing datasets that explicitly focus on multi-hop reasoning are lacking in learning multi-tasking. |
| Approach: | They propose to use sentence-factored models to solve multi-hop question answering tasks . they find spurious correlations in unmasked versions of WikiHop and HotpotQA . |
| Outcome: | The proposed datasets are used to test models on multi-hop question answering tasks. |