Augmenting Researchy Questions with Sub-question Judgments
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866917041360863232 |
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| author | Ju, Jia-Huei Yang, Eugene Adriaanse, Trevor Yates, Andrew |
| author_facet | Ju, Jia-Huei Yang, Eugene Adriaanse, Trevor Yates, Andrew |
| contents | The Researchy Questions dataset provides about 100k question queries with complex information needs that require retrieving information about several aspects of a topic. Each query in ResearchyQuestions is associated with sub-questions that were produced by prompting GPT-4. While ResearchyQuestions contains labels indicating what documents were clicked after issuing the query, there are no associations in the dataset between sub-questions and relevant documents. In this work, we augment the Researchy Questions dataset with LLM-judged labels for each sub-question using a Llama3.3 70B model. We intend these sub-question labels to serve as a resource for training retrieval models that better support complex information needs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21733 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Augmenting Researchy Questions with Sub-question Judgments Ju, Jia-Huei Yang, Eugene Adriaanse, Trevor Yates, Andrew Information Retrieval The Researchy Questions dataset provides about 100k question queries with complex information needs that require retrieving information about several aspects of a topic. Each query in ResearchyQuestions is associated with sub-questions that were produced by prompting GPT-4. While ResearchyQuestions contains labels indicating what documents were clicked after issuing the query, there are no associations in the dataset between sub-questions and relevant documents. In this work, we augment the Researchy Questions dataset with LLM-judged labels for each sub-question using a Llama3.3 70B model. We intend these sub-question labels to serve as a resource for training retrieval models that better support complex information needs. |
| title | Augmenting Researchy Questions with Sub-question Judgments |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2510.21733 |