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        <title>Frequency Balanced Datasets Lead to Better Language Models</title>
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        <namePart type="given">Rodolfo</namePart>
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    <abstract>This paper reports on the experiments aimed to improve our understanding of the role of the amount of data required for training attention-based transformer language models. Specifically, we investigate the impact of reducing the immense amounts of required pre-training data through sampling strategies that identify and reduce high-frequency tokens as different studies have indicated that the existence of very high-frequency tokens in pre-training data might bias learning, causing undesired effects. In this light, we describe our sampling algorithm that iteratively assesses token frequencies and removes sentences that contain still high-frequency tokens, eventually delivering a balanced, linguistically correct dataset. We evaluate the results in terms of model perplexity and fine-tuning linguistic probing tasks, NLP downstream tasks as well as more semantic SuperGlue tasks. The results show that pre-training with the resulting balanced dataset allows reducing up to three times the pre-training data.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.527</identifier>
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%0 Conference Proceedings
%T Frequency Balanced Datasets Lead to Better Language Models
%A Zevallos, Rodolfo
%A Farrús, Mireia
%A Bel, Núria
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zevallos-etal-2023-frequency
%X This paper reports on the experiments aimed to improve our understanding of the role of the amount of data required for training attention-based transformer language models. Specifically, we investigate the impact of reducing the immense amounts of required pre-training data through sampling strategies that identify and reduce high-frequency tokens as different studies have indicated that the existence of very high-frequency tokens in pre-training data might bias learning, causing undesired effects. In this light, we describe our sampling algorithm that iteratively assesses token frequencies and removes sentences that contain still high-frequency tokens, eventually delivering a balanced, linguistically correct dataset. We evaluate the results in terms of model perplexity and fine-tuning linguistic probing tasks, NLP downstream tasks as well as more semantic SuperGlue tasks. The results show that pre-training with the resulting balanced dataset allows reducing up to three times the pre-training data.
%R 10.18653/v1/2023.findings-emnlp.527
%U https://aclanthology.org/2023.findings-emnlp.527
%U https://doi.org/10.18653/v1/2023.findings-emnlp.527
%P 7859-7872

Markdown (Informal)

[Frequency Balanced Datasets Lead to Better Language Models](https://aclanthology.org/2023.findings-emnlp.527) (Zevallos et al., Findings 2023)

• Frequency Balanced Datasets Lead to Better Language Models (Zevallos et al., Findings 2023)

ACL

• Rodolfo Zevallos, Mireia Farrús, and Núria Bel. 2023. Frequency Balanced Datasets Lead to Better Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7859–7872, Singapore. Association for Computational Linguistics.