MGen: Millions of Naturally Occurring Generics in Context
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Abstract (arXiv)
MGen is a dataset of over 4 million naturally occurring generic and quantified sentences extracted from diverse textual sources. Sentences in the dataset have long context documents, corresponding to websites and academic papers, and cover 11 different quantifiers. We analyze the features of generics sentences in the dataset, with interesting insights: generics can be long sentences (averaging over 16 words) and speakers often use them to express generalisations about people. MGen is the biggest and most diverse dataset of naturally occurring generic sentences, opening the door to large-scale computational research on genericity. It is publicly available at https://gustavocilleruelo.com/mgen
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 58 references that are in the map. First posted on arXiv 30 Sep 2025.
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9 of 58 references.
References · 9
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
- 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,077
- 2023The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Onlycited by 763
- 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723
- 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 472
- 2020Evaluating the Evaluation of Diversity in Natural Language Generationcited by 129
- 2020GenericsKB: A Knowledge Base of Generic Statementscited by 51
- 2022Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptionscited by 17
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Citing papers per year: 2026: 1.
1 paper.