Familiarizing oneself with a new scientific field and its existing literature can be daunting due to the large amount of available articles. Curated lists of academic references, or reading lists, compiled by experts, offer a structured way to gain a comprehensive overview of a domain or a specific scientific challenge. In this work, we introduce ACL-rlg, the largest open expert-annotated reading list dataset. We also provide multiple baselines for evaluating reading list generation and formally define it as a retrieval task. Our qualitative study highlights the fact that traditional scholarly search engines and indexing methods perform poorly on this task, and GPT-4o, despite showing better results, exhibits signs of potential data contamination.
@article{arxiv.2502.15692,
title = {ACL-rlg: A Dataset for Reading List Generation},
author = {Julien Aubert-Béduchaud and Florian Boudin and Béatrice Daille and Richard Dufour},
journal= {arXiv preprint arXiv:2502.15692},
year = {2025}
}