Papers › An Evaluation Framework for Attributed Information Retrieval using Large Language Models
An Evaluation Framework for Attributed Information Retrieval using Large Language Models
Hanane Djeddal, Pierre Erbacher, Raouf Toukal, Laure Soulier, Karen Pinel-Sauvagnat, Sophia Katrenko, Lynda Tamine
With the growing success of Large Language models (LLMs) in information-seeking scenarios, search engines are now adopting generative approaches to provide answers along with in-line citations as attribution. While existing work focuses mainly on attributed question answering, in this paper, we target information-seeking scenarios which are often more challenging due to the open-ended nature of the queries and the size of the label space in terms of the diversity of candidate-attributed answers per query. We propose a reproducible framework to evaluate and benchmark attributed information seeking, using any backbone LLM, and different architectural designs: (1) Generate (2) Retrieve then Generate, and (3) Generate then Retrieve. Experiments using HAGRID, an attributed information-seeking dataset, show the impact of different scenarios on both the correctness and attributability of answers.
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