Interactive Information Need Prediction with Intent and Context
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915092020330496 |
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| author | Ros, Kevin Pandya, Dhyey Zhai, ChengXiang |
| author_facet | Ros, Kevin Pandya, Dhyey Zhai, ChengXiang |
| contents | The ability to predict a user's information need would have wide-ranging implications, from saving time and effort to mitigating vocabulary gaps. We study how to interactively predict a user's information need by letting them select a pre-search context (e.g., a paragraph, sentence, or singe word) and specify an optional partial search intent (e.g., "how", "why", "applications", etc.). We examine how various generative language models can explicitly make this prediction by generating a question as well as how retrieval models can implicitly make this prediction by retrieving an answer. We find that this prediction process is possible in many cases and that user-provided partial search intent can help mitigate large pre-search contexts. We conclude that this framework is promising and suitable for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02635 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Interactive Information Need Prediction with Intent and Context Ros, Kevin Pandya, Dhyey Zhai, ChengXiang Information Retrieval The ability to predict a user's information need would have wide-ranging implications, from saving time and effort to mitigating vocabulary gaps. We study how to interactively predict a user's information need by letting them select a pre-search context (e.g., a paragraph, sentence, or singe word) and specify an optional partial search intent (e.g., "how", "why", "applications", etc.). We examine how various generative language models can explicitly make this prediction by generating a question as well as how retrieval models can implicitly make this prediction by retrieving an answer. We find that this prediction process is possible in many cases and that user-provided partial search intent can help mitigate large pre-search contexts. We conclude that this framework is promising and suitable for real-world applications. |
| title | Interactive Information Need Prediction with Intent and Context |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2501.02635 |