An Evaluation Framework for Attributed Information Retrieval using Large Language Models
<|reference_start|>An Evaluation Framework for Attributed Information Retrieval using Large Language Models: 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.<|reference_end|>
@article{djeddal2024an,
    title={An Evaluation Framework for Attributed Information Retrieval using Large
  Language Models},
    author={Hanane Djeddal, Pierre Erbacher, Raouf Toukal, Laure Soulier, Karen
  Pinel-Sauvagnat, Sophia Katrenko, Lynda Tamine},
    journal={arXiv preprint arXiv:2409.08014},
    year={2024},
    doi={10.1145/3627673.3679172},
    archivePrefix={arXiv},
    eprint={2409.08014},
    primaryClass={cs.IR}
}