Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model

• URL: http://arxiv.org/abs/2501.05122v1
• Date: Thu, 09 Jan 2025 10:26:14 GMT
• Title: Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model
• Authors: Gregor Geigle, Florian Schneider, Carolin Holtermann, Chris Biemann, Radu Timofte, Anne Lauscher, Goran Glavaš,
• Abstract summary: Most Large Vision-Language Models (LVLMs) to date are trained predominantly on English data. We investigate how different training mixes tip the scale for different groups of languages. We train Centurio, a 100-language LVLM, offering state-of-the-art performance in an evaluation covering 14 tasks and 56 languages.
• Score: 66.17354128553244
• License:
• Abstract: Most Large Vision-Language Models (LVLMs) to date are trained predominantly on English data, which makes them struggle to understand non-English input and fail to generate output in the desired target language. Existing efforts mitigate these issues by adding multilingual training data, but do so in a largely ad-hoc manner, lacking insight into how different training mixes tip the scale for different groups of languages. In this work, we present a comprehensive investigation into the training strategies for massively multilingual LVLMs. First, we conduct a series of multi-stage experiments spanning 13 downstream vision-language tasks and 43 languages, systematically examining: (1) the number of training languages that can be included without degrading English performance and (2) optimal language distributions of pre-training as well as (3) instruction-tuning data. Further, we (4) investigate how to improve multilingual text-in-image understanding, and introduce a new benchmark for the task. Surprisingly, our analysis reveals that one can (i) include as many as 100 training languages simultaneously (ii) with as little as 25-50\% of non-English data, to greatly improve multilingual performance while retaining strong English performance. We further find that (iii) including non-English OCR data in pre-training and instruction-tuning is paramount for improving multilingual text-in-image understanding. Finally, we put all our findings together and train Centurio, a 100-language LVLM, offering state-of-the-art performance in an evaluation covering 14 tasks and 56 languages.

Related papers

• EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models [50.459861376459656]
  EMMA-500 is a large-scale multilingual language model continue-trained on texts across 546 languages. Our results highlight the effectiveness of continual pre-training in expanding large language models' language capacity.
  arXiv  Detail & Related papers  (2024-09-26T14:40:45Z)
• Decomposed Prompting: Unveiling Multilingual Linguistic Structure Knowledge in English-Centric Large Language Models [12.700783525558721]
  English-centric Large Language Models (LLMs) like GPT-3 and LLaMA display a remarkable ability to perform multilingual tasks. This paper introduces the decomposed prompting approach to probe the linguistic structure understanding of these LLMs in sequence labeling tasks.
  arXiv  Detail & Related papers  (2024-02-28T15:15:39Z)
• Turning English-centric LLMs Into Polyglots: How Much Multilinguality Is Needed? [40.13166574854085]
  We investigate the minimal amount of multilinguality required to elicit cross-lingual generalisation in English-centric large language models. We find that multilingual instruction tuning with as few as two to three languages is both necessary and sufficient to elicit effective cross-lingual generalisation.
  arXiv  Detail & Related papers  (2023-12-20T00:49:52Z)
• Towards a Deep Understanding of Multilingual End-to-End Speech Translation [52.26739715012842]
  We analyze representations learnt in a multilingual end-to-end speech translation model trained over 22 languages. We derive three major findings from our analysis.
  arXiv  Detail & Related papers  (2023-10-31T13:50:55Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• Improved Self-Supervised Multilingual Speech Representation Learning Combined with Auxiliary Language Information [21.250763472985824]
  Self-supervised multilingual speech representation learning has shown success in improving the performance of multilingual automatic speech recognition. However, similar to the supervised learning, multilingual pre-training may also suffer from language interference. We introduce several techniques for improving self-supervised multilingual pre-training by leveraging auxiliary language information.
  arXiv  Detail & Related papers  (2022-12-07T06:18:59Z)
• Bootstrapping Multilingual Semantic Parsers using Large Language Models [28.257114724384806]
  translate-train paradigm of transferring English datasets across multiple languages remains to be the key ingredient for training task-specific multilingual models. We consider the task of multilingual semantic parsing and demonstrate the effectiveness and flexibility offered by large language models (LLMs) for translating English datasets into several languages via few-shot prompting.
  arXiv  Detail & Related papers  (2022-10-13T19:34:14Z)
• Generalizing Multimodal Pre-training into Multilingual via Language Acquisition [54.69707237195554]
  English-based Vision-Language Pre-training has achieved great success in various downstream tasks. Some efforts have been taken to generalize this success to non-English languages through Multilingual Vision-Language Pre-training. We propose a textbfMultitextbfLingual textbfAcquisition (MLA) framework that can easily generalize a monolingual Vision-Language Pre-training model into multilingual.
  arXiv  Detail & Related papers  (2022-05-29T08:53:22Z)
• 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)

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.