Assessing the Role of Data Quality in Training Bilingual Language Models

• URL: http://arxiv.org/abs/2506.12966v1
• Date: Sun, 15 Jun 2025 21:08:51 GMT
• Title: Assessing the Role of Data Quality in Training Bilingual Language Models
• Authors: Skyler Seto, Maartje ter Hoeve, Maureen de Seyssel, David Grangier,
• Abstract summary: We show that unequal data quality, not just data quantity, is a major driver of performance degradation in bilingual settings.<n>We propose a simple yet effective data filtering strategy to select higher-quality bilingual training data with only high quality English data.
• Score: 17.603371705571107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bilingual and multilingual language models offer a promising path toward scaling NLP systems across diverse languages and users. However, their performance often varies wildly between languages as prior works show that adding more languages can degrade performance for some languages (such as English), while improving others (typically more data constrained languages). In this work, we investigate causes of these inconsistencies by comparing bilingual and monolingual language models. Our analysis reveals that unequal data quality, not just data quantity, is a major driver of performance degradation in bilingual settings. We propose a simple yet effective data filtering strategy to select higher-quality bilingual training data with only high quality English data. Applied to French, German, and Chinese, our approach improves monolingual performance by 2-4% and reduces bilingual model performance gaps to 1%. These results highlight the overlooked importance of data quality in multilingual pretraining and offer a practical recipe for balancing performance.

Related papers

• Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models [52.22235443948351]
  High-quality multilingual training data is essential for effectively pretraining large language models (LLMs)<n>Here, we introduce JQL, a systematic approach that efficiently curates diverse and high-quality multilingual data at scale.<n>JQL distills LLMs' annotation capabilities into lightweight annotators based on pretrained multilingual embeddings.
  arXiv  Detail & Related papers  (2025-05-28T11:06:54Z)
• When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages [25.52470575274251]
  We pre-train over 10,000 monolingual and multilingual language models for over 250 languages. We find that in moderation, adding multilingual data improves low-resource language modeling performance. As dataset sizes increase, adding multilingual data begins to hurt performance for both low-resource and high-resource languages.
  arXiv  Detail & Related papers  (2023-11-15T18:47:42Z)
• GradSim: Gradient-Based Language Grouping for Effective Multilingual Training [13.730907708289331]
  We propose GradSim, a language grouping method based on gradient similarity. Our experiments on three diverse multilingual benchmark datasets show that it leads to the largest performance gains. Besides linguistic features, the topics of the datasets play an important role for language grouping.
  arXiv  Detail & Related papers  (2023-10-23T18:13:37Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning [14.02101305717738]
  Multilingual large language models (MLLMs) are jointly trained on data from many different languages. It remains unclear to what extent, and under which conditions, languages rely on each other's data. We find that MLLMs rely on data from multiple languages from the early stages of fine-tuning and that this reliance gradually increases as fine-tuning progresses.
  arXiv  Detail & Related papers  (2023-05-22T17:47:41Z)
• Multilingual Neural Semantic Parsing for Low-Resourced Languages [1.6244541005112747]
  We introduce a new multilingual semantic parsing dataset in English, Italian and Japanese. We show that joint multilingual training with pretrained encoders substantially outperforms our baselines on the TOP dataset. We find that a semantic trained only on English data achieves a zero-shot performance of 44.9% exact-match accuracy on Italian sentences.
  arXiv  Detail & Related papers  (2021-06-07T09:53:02Z)
• Are Multilingual Models Effective in Code-Switching? [57.78477547424949]
  We study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting. Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching.
  arXiv  Detail & Related papers  (2021-03-24T16:20:02Z)
• How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models [96.32118305166412]
  We study a set of nine typologically diverse languages with readily available pretrained monolingual models on a set of five diverse monolingual downstream tasks. We find that languages which are adequately represented in the multilingual model's vocabulary exhibit negligible performance decreases over their monolingual counterparts.
  arXiv  Detail & Related papers  (2020-12-31T14:11:00Z)
• Multilingual Translation with Extensible Multilingual Pretraining and Finetuning [77.33262578776291]
  Previous work has demonstrated that machine translation systems can be created by finetuning on bitext. We show that multilingual translation models can be created through multilingual finetuning. We demonstrate that pretrained models can be extended to incorporate additional languages without loss of performance.
  arXiv  Detail & Related papers  (2020-08-02T05:36:55Z)
• Balancing Training for Multilingual Neural Machine Translation [130.54253367251738]
  multilingual machine translation (MT) models can translate to/from multiple languages. Standard practice is to up-sample less resourced languages to increase representation. We propose a method that instead automatically learns how to weight training data through a data scorer.
  arXiv  Detail & Related papers  (2020-04-14T18:23:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.