--- license: cc-by-4.0 task_categories: - question-answering language: - en tags: - multi-hop pretty_name: MoreHopQA size_categories: - 1K We propose a new multi-hop dataset, MoreHopQA, which shifts from extractive to generative answers. Our dataset is created by utilizing three existing multi-hop datasets: [HotpotQA](https://github.com/hotpotqa/hotpot), [2Wiki-MultihopQA](https://github.com/Alab-NII/2wikimultihop), and [MuSiQue](https://github.com/StonyBrookNLP/musique). Instead of relying solely on factual reasoning, we enhance the existing multi-hop questions by adding another layer of questioning.
## Dataset Details ### Dataset Description Our dataset is created through a semi-automated process, resulting in a dataset with 1118 samples that have undergone human verification. For each sample, we share our 6 evaluation cases, including the new question, the original question, all the necessary subquestions, and a composite question from the second entity to the final answer (case 3 above). We share both a version where each question was verified by a human, and a larger, solely automatically generated version ("unverified"). We recommend to primarily use the human-verified version. - **Curated by:** Aizawa Lab, National Institute of Informatics (NII), Tokyo, Japan - **Language(s) (NLP):** English -

License: The MorehopQA dataset is licensed under CC BY 4.0

### Dataset Sources - **Repository:** [github.com/Alab-NII/morehopqa](https://github.com/Alab-NII/morehopqa) - **Paper:** [More Information Needed] ## Uses We provide our dataset to the community and hope that other researchers find it a useful tool to analyze and improve the multi-hop reasoning capabilities of their models. MoreHopQA is designed to challenge systems with complex queries requiring synthesis from multiple sources, thereby advancing the field in understanding and generating nuanced, context-rich responses. Additionally, we aim for this dataset to spur further innovation in reasoning models, helping to bridge the gap between human-like understanding and AI capabilities. ## Dataset Structure We share both a version where each question was verified by a human, and a larger, solely automatically generated version ("unverified"). We recommend to primarily use the human-verified version, which is also the default option when loading the dataset. Each sample in the dataset contains the following fields: - **question**: Our new multi-hop question with added reasoning (case 1 above) - **answer**: The answer to the last hop (case 1, 3 and 4 above) - **context**: Relevant context information to answer the previous question (relevant for all cases except case 4) - **previous_question**: The previous 2-hop question from the original dataset (case 2 above) - **previous_answer**: The answer to the previous 2-hop question (case 2 and 5 above) - **question_decomposition**: Each question of the reasoning chain. List of entries with keys "sub_id" (position in the chain), "question", "answer", "paragraph_support_title" (relevant context paragraph). (sub_id 1 → case 6; sub_id 2 → case 5; sub_id 3 → case 4) - **question_on_last_hop**: Question for case 3 above - **answer_type**: Type of the expected answer - **previous_answer_type**: Type of the answer to the previous 2-hop question - **no_of_hops**: Number of extra hops to answer the additional reasoning question (might be more than one for more complicated tasks) - **reasoning_type**: Might contain "Symbolic", "Arithmetic", "Commonsense"; depending on which kind of reasoning is required for the additional reasoning ## Dataset Creation ### Source Data Our dataset is created by utilizing three existing multi-hop datasets: [HotpotQA](https://github.com/hotpotqa/hotpot), [2Wiki-MultihopQA](https://github.com/Alab-NII/2wikimultihop), and [MuSiQue](https://github.com/StonyBrookNLP/musique) ## Citation If you find this dataset helpful, please consider citing our paper **BibTeX:** [More Information Needed]