通过用户选择上下文和部分意图，预测其信息需求。
Interactive Information Need Prediction with Intent and Context
- 用户可选上下文与部分意图，辅助系统预测需求。
- 生成模型能直接生成问题，检索模型间接推断答案。
- 部分意图可缓解长上下文带来的干扰，适合实际应用。
预测用户的潜在信息需求具有重要意义，可节省时间并弥补词汇差距。本文研究如何通过交互方式预测用户的信息需求：允许用户选择预搜索上下文（如段落、句子或单个词），并指定可选的部分搜索意图（如“如何”、“为什么”、“应用”等）。我们评估了多种生成式语言模型如何通过生成问题来显式预测需求，以及检索模型如何通过返回答案来隐式完成预测。实验表明，该预测在多数情况下可行，且用户提供的部分搜索意图有助于缓解长上下文带来的影响。结论认为该框架具有前景，适用于真实场景。
原文摘要 · Abstract (English)
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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