Retrieval-augmented generation (RAG) has proven effective in conditioning the output of large language models (LLMs) on relevant documents and for grounding LLM-generated statements, this way combatting the so-called confabulation or hallucination problem. Basically, RAG combines (1) a retrieval phase, where a search system identifies relevant documents for a user prompt, and (2) a generation phase, where an LLM synthesizes a tailored answer, probably linking to the retrieved sources.

RAG challenges “classical” retrieval technology and has the potential to revolutionize information-seeking behavior overall by reducing a searcher's effort to extract the desired information from individual search results. The revolution becomes evident, among others, in a change in the design of search engine results pages (SERPs): Instead of presenting the proverbial list of “ten blue links”, the classic list SERP, a generated text with references is shown, a text SERP. The first public prototype of this kind was You.com’s You Chat, followed by Microsoft’s Copilot, Google’s Gemini, Baidu’s Ernie, and many others. Still, plenty of unsolved problems and interesting research questions lurk under the hood of this new user interface.

This Dagstuhl Seminar will focus on the expectations, the promises, the potential, and the limits of integrating RAG in search systems. Relevant questions include

• Will we ever search again?
• Will RAG bias retrieval results?
• Is RAG more than fact checking for conversational search?
• How can we measure the effectiveness of RAG-based systems?
• How can we keep RAG-based systems transparent and accountable?

To work on these and related questions, the Dagstuhl Seminar will bring together experts from the fields of information retrieval, natural language processing, and generative AI who have academic, industrial, or non-profit backgrounds.

Creative Commons BY 4.0

Matthias Hagen, Josiane Mothe, Smaranda Muresan, Martin Potthast, Benno Stein and Min Zhang