Preferences for Idiomatic Language are Acquired Slowly -- and Forgotten Quickly: A Case Study on Swedish

• URL: http://arxiv.org/abs/2602.03484v1
• Date: Tue, 03 Feb 2026 12:57:39 GMT
• Title: Preferences for Idiomatic Language are Acquired Slowly -- and Forgotten Quickly: A Case Study on Swedish
• Authors: Jenny Kunz,
• Abstract summary: We investigate how language models develop preferences for textitidiomatic as compared to textitlinguistically acceptable Swedish.<n>For linguistic acceptability, we adapt existing benchmarks into a minimal-pair format.<n>Our findings suggest that idiomatic competence emerges more slowly than other linguistic abilities.
• Score: 0.6599344783327054
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this study, we investigate how language models develop preferences for \textit{idiomatic} as compared to \textit{linguistically acceptable} Swedish, both during pretraining and when adapting a model from English to Swedish. To do so, we train models on Swedish from scratch and by fine-tuning English-pretrained models, probing their preferences at various checkpoints using minimal pairs that differ in linguistic acceptability or idiomaticity. For linguistic acceptability, we adapt existing benchmarks into a minimal-pair format. To assess idiomaticity, we introduce two novel datasets: one contrasting conventionalized idioms with plausible variants, and another contrasting idiomatic Swedish with Translationese. Our findings suggest that idiomatic competence emerges more slowly than other linguistic abilities, including grammatical and lexical correctness. While longer training yields diminishing returns for most tasks, idiom-related performance continues to improve, particularly in the largest model tested (8B). However, instruction tuning on data machine-translated from English -- the common approach for languages with little or no native instruction data -- causes models to rapidly lose their preference for idiomatic language.

Related papers

• False Friends Are Not Foes: Investigating Vocabulary Overlap in Multilingual Language Models [53.01170039144264]
  Subword tokenizers trained on multilingual corpora naturally produce overlapping tokens across languages.<n>Does token overlap facilitate cross-lingual transfer or instead introduce interference between languages?<n>We find that models with overlap outperform models with disjoint vocabularies.
  arXiv  Detail & Related papers  (2025-09-23T07:47:54Z)
• Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings [1.1556013985948772]
  We evaluate transferability of pre-trained language models to low-resource Indonesian local languages.<n>We group the target languages into three categories: seen, partially seen, and unseen.<n> Multilingual models perform best on seen languages, moderately on partially seen ones, and poorly on unseen languages.<n>We find that MAD-X significantly improves performance, especially for seen and partially seen languages, without requiring labeled data in the target language.
  arXiv  Detail & Related papers  (2025-07-02T12:17:55Z)
• PreAlign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment [68.20851615263953]
  Large language models demonstrate reasonable multilingual abilities, despite predominantly English-centric pretraining. The spontaneous multilingual alignment in these models is shown to be weak, leading to unsatisfactory cross-lingual transfer and knowledge sharing. We propose PreAlign, a framework that establishes multilingual alignment prior to language model pretraining.
  arXiv  Detail & Related papers  (2024-07-23T06:59:53Z)
• Robustifying Language Models with Test-Time Adaptation [17.96043752001886]
  Large-scale language models achieved state-of-the-art performance over a number of language tasks. They fail on adversarial language examples, which are sentences optimized to fool the language models but with similar semantic meanings for humans. We show that we can reverse many language adversarial attacks by adapting the input sentence with predictions from masked words.
  arXiv  Detail & Related papers  (2023-10-29T22:37:54Z)
• Accidental Learners: Spoken Language Identification in Multilingual Self-Supervised Models [11.439430077017635]
  We find that pre-trained speech models optimally encode language discriminatory information in lower layers. We demonstrate that the embeddings obtained from these layers are significantly robust to classify unseen languages. We open-source the model through the NVIDIA NeMo toolkit.
  arXiv  Detail & Related papers  (2022-11-09T18:53:59Z)
• Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models [73.11488464916668]
  This study investigates the dynamics of the multilingual pretraining process. We probe checkpoints taken from throughout XLM-R pretraining, using a suite of linguistic tasks. Our analysis shows that the model achieves high in-language performance early on, with lower-level linguistic skills acquired before more complex ones.
  arXiv  Detail & Related papers  (2022-05-24T03:35:00Z)
• Language Contamination Explains the Cross-lingual Capabilities of English Pretrained Models [79.38278330678965]
  We find that common English pretraining corpora contain significant amounts of non-English text. This leads to hundreds of millions of foreign language tokens in large-scale datasets. We then demonstrate that even these small percentages of non-English data facilitate cross-lingual transfer for models trained on them.
  arXiv  Detail & Related papers  (2022-04-17T23:56:54Z)
• Language Models are Few-shot Multilingual Learners [66.11011385895195]
  We evaluate the multilingual skills of the GPT and T5 models in conducting multi-class classification on non-English languages. We show that, given a few English examples as context, pre-trained language models can predict not only English test samples but also non-English ones.
  arXiv  Detail & Related papers  (2021-09-16T03:08:22Z)
• Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages [81.90356787324481]
  Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected.
  arXiv  Detail & Related papers  (2020-10-20T17:31:07Z)
• Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment [17.995905582226463]
  We compare model performance in English and Spanish to show that non-linguistic biases in RNN LMs advantageously overlap with syntactic structure in English but not Spanish. English models may appear to acquire human-like syntactic preferences, while models trained on Spanish fail to acquire comparable human-like preferences.
  arXiv  Detail & Related papers  (2020-05-01T01:21:47Z)

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.