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arXiv · 2502.15692

ACL-rlg: A Dataset for Reading List Generation

Julien Aubert-Béduchaud·Florian Boudin·Béatrice Daille·Richard Dufour
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Abstract

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.

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Julien Aubert-Béduchaud, Florian Boudin, Béatrice Daille, Richard Dufour. 2024-12-30. ACL-rlg: A Dataset for Reading List Generation. https://arxiv.org/abs/2502.15692

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