An Evaluation Framework for Attributed Information Retrieval using Large Language Models

• URL: http://arxiv.org/abs/2409.08014v1
• Date: Thu, 12 Sep 2024 12:57:08 GMT
• Title: An Evaluation Framework for Attributed Information Retrieval using Large Language Models
• Authors: Hanane Djeddal, Pierre Erbacher, Raouf Toukal, Laure Soulier, Karen Pinel-Sauvagnat, Sophia Katrenko, Lynda Tamine,
• Abstract summary: We propose a framework to evaluate and benchmark attributed information seeking. Experiments using HAGRID, an attributed information-seeking dataset, show the impact of different scenarios on the correctness and attributability of answers.
• Score: 5.216296688442701
• License:
• Abstract: 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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