# An Evaluation Framework for Attributed Information Retrieval using Large Language Models

arXiv: https://arxiv.org/abs/2409.08014 · HTML: https://mlsanity.com/paper/2409.08014/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2024.

## Built on by (most cited first, 3 of 3)

- [Large Language Model Sourcing: A Survey](https://mlsanity.com/paper/2510.10161/) (2025)
- [ConCISE: A Reference-Free Conciseness Evaluation Metric for LLM-Generated Answers](https://mlsanity.com/paper/2511.16846/) (2025)
- [Jointly Generating and Attributing Answers using Logits of Document-Identifier Tokens](https://mlsanity.com/paper/2508.08942/) (2025)

## Abstract (arXiv)

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.