Recent advances in large language models have enabled the development of
viable generative information retrieval systems. A generative retrieval system
returns a grounded generated text in response to an information need instead of
the traditional document ranking. Quantifying the utility of these types of
responses is essential for evaluating generative retrieval systems. As the
established evaluation methodology for ranking-based ad hoc retrieval may seem
unsuitable for generative retrieval, new approaches for reliable, repeatable,
and reproducible experimentation are required. In this paper, we survey the
relevant information retrieval and natural language processing literature,
identify search tasks and system architectures in generative retrieval, develop
a corresponding user model, and study its operationalization. This theoretical
analysis provides a foundation and new insights for the evaluation of
generative ad hoc retrieval systems.

通过调查信息检索和自然语言处理文献，识别生成式检索中的搜索任务和系统架构，开发相应的用户模型并研究其操作化，该文理论分析为生成式即时检索系统的评估提供了基础和新洞察。