Evaluating Generative Ad Hoc Information Retrieval
Recent advances in large language models have enabled the development of viable generative retrieval systems. Instead of a traditional document ranking, many generative retrieval systems directly return a grounded generated text as an answer to an information need expressed as a query or question. Quantifying the utility of the textual responses is essential for appropriately evaluating such generative ad hoc retrieval. Yet, the established evaluation methodology for ranking-based retrieval is not suited for reliable, repeatable, and reproducible evaluation of generated answers. In this paper, we survey the relevant literature from the fields of information retrieval and natural language processing, we identify search tasks and system architectures in generative retrieval, we develop a corresponding user model, and we study its operationalization. Our analysis provides a foundation and new insights for the evaluation of generative retrieval systems, focusing on ad hoc retrieval.
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