Interactive Information Need Prediction with Intent and Context

• URL: http://arxiv.org/abs/2501.02635v1
• Date: Sun, 05 Jan 2025 19:47:57 GMT
• Title: Interactive Information Need Prediction with Intent and Context
• Authors: Kevin Ros, Dhyey Pandya, ChengXiang Zhai,
• Abstract summary: We study how to interactively predict a user's information need by letting them select a pre-search context.<n>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.
• Score: 21.033863934962053
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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