ACL-rlg: A Dataset for Reading List Generation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912241312333824 |
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| author | Aubert-Béduchaud, Julien Boudin, Florian Daille, Béatrice Dufour, Richard |
| author_facet | Aubert-Béduchaud, Julien Boudin, Florian Daille, Béatrice Dufour, Richard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15692 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ACL-rlg: A Dataset for Reading List Generation Aubert-Béduchaud, Julien Boudin, Florian Daille, Béatrice Dufour, Richard Digital Libraries Artificial Intelligence Computation and Language Information Retrieval 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. |
| title | ACL-rlg: A Dataset for Reading List Generation |
| topic | Digital Libraries Artificial Intelligence Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2502.15692 |