A Post-trainer's Guide to Multilingual Training Data: Uncovering Cross-lingual Transfer Dynamics

• URL: http://arxiv.org/abs/2504.16677v1
• Date: Wed, 23 Apr 2025 12:52:49 GMT
• Title: A Post-trainer's Guide to Multilingual Training Data: Uncovering Cross-lingual Transfer Dynamics
• Authors: Luisa Shimabucoro, Ahmet Ustun, Marzieh Fadaee, Sebastian Ruder,
• Abstract summary: This study examines cross-lingual transfer dynamics in realistic post-training settings.<n>We study two model families of up to 35B parameters in size trained on mixtures of multilingual data.
• Score: 40.60487538069713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In order for large language models to be useful across the globe, they are fine-tuned to follow instructions on multilingual data. Despite the ubiquity of such post-training, a clear understanding of the dynamics that enable cross-lingual transfer remains elusive. This study examines cross-lingual transfer (CLT) dynamics in realistic post-training settings. We study two model families of up to 35B parameters in size trained on carefully controlled mixtures of multilingual data on three generative tasks with varying levels of complexity (summarization, instruction following, and mathematical reasoning) in both single-task and multi-task instruction tuning settings. Overall, we find that the dynamics of cross-lingual transfer and multilingual performance cannot be explained by isolated variables, varying depending on the combination of post-training settings. Finally, we identify the conditions that lead to effective cross-lingual transfer in practice.

Related papers

• CrossIn: An Efficient Instruction Tuning Approach for Cross-Lingual Knowledge Alignment [38.35458193262633]
  English-centric models are usually suboptimal in other languages.<n>We propose a novel approach called CrossIn, which utilizes a mixed composition of cross-lingual instruction tuning data.
  arXiv  Detail & Related papers  (2024-04-18T06:20:50Z)
• Zero-shot Cross-lingual Transfer without Parallel Corpus [6.937772043639308]
  We propose a novel approach to conduct zero-shot cross-lingual transfer with a pre-trained model. It consists of a Bilingual Task Fitting module that applies task-related bilingual information alignment. A self-training module generates pseudo soft and hard labels for unlabeled data and utilizes them to conduct self-training.
  arXiv  Detail & Related papers  (2023-10-07T07:54:22Z)
• Languages You Know Influence Those You Learn: Impact of Language Characteristics on Multi-Lingual Text-to-Text Transfer [4.554080966463776]
  Multi-lingual language models (LM) have been remarkably successful in enabling natural language tasks in low-resource languages. We try to better understand how such models, specifically mT5, transfer *any* linguistic and semantic knowledge across languages. A key finding of this work is that similarity of syntax, morphology and phonology are good predictors of cross-lingual transfer.
  arXiv  Detail & Related papers  (2022-12-04T07:22:21Z)
• 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)
• Cross-lingual Lifelong Learning [53.06904052325966]
  We present a principled Cross-lingual Continual Learning (CCL) evaluation paradigm. We provide insights into what makes multilingual sequential learning particularly challenging. The implications of this analysis include a recipe for how to measure and balance different cross-lingual continual learning desiderata.
  arXiv  Detail & Related papers  (2022-05-23T09:25:43Z)
• Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation [80.16548523140025]
  We extend the vanilla pretrain-finetune pipeline with extra code-switching restore task to bridge the gap between the pretrain and finetune stages. Our approach could narrow the cross-lingual sentence representation distance and improve low-frequency word translation with trivial computational cost.
  arXiv  Detail & Related papers  (2022-04-16T16:08:38Z)
• Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer [14.799109368073548]
  We exploit the pre-trained seq2seq model mBART for multilingual text style transfer. Using machine translated data as well as gold aligned English sentences yields state-of-the-art results.
  arXiv  Detail & Related papers  (2022-03-16T11:27:48Z)
• First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT [2.2931318723689276]
  Cross-lingual transfer emerges from fine-tuning on a task of interest in one language and evaluating on a distinct language, not seen during the fine-tuning. We show that multilingual BERT can be viewed as the stacking of two sub-networks: a multilingual encoder followed by a task-specific language-agnostic predictor. While the encoder is crucial for cross-lingual transfer and remains mostly unchanged during fine-tuning, the task predictor has little importance on the transfer and can be red during fine-tuning.
  arXiv  Detail & Related papers  (2021-01-26T22:12:38Z)
• VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation [77.82373082024934]
  We plug a cross-attention module into the Transformer encoder to explicitly build the interdependence between languages. It can effectively avoid the degeneration of predicting masked words only conditioned on the context in its own language. The proposed cross-lingual model delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark.
  arXiv  Detail & Related papers  (2020-10-30T03:41:38Z)
• Cross-lingual Spoken Language Understanding with Regularized Representation Alignment [71.53159402053392]
  We propose a regularization approach to align word-level and sentence-level representations across languages without any external resource. Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios.
  arXiv  Detail & Related papers  (2020-09-30T08:56:53Z)

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.