Overcoming Vocabulary Constraints with Pixel-level Fallback

• URL: http://arxiv.org/abs/2504.02122v1
• Date: Wed, 02 Apr 2025 20:50:31 GMT
• Title: Overcoming Vocabulary Constraints with Pixel-level Fallback
• Authors: Jonas F. Lotz, Hendra Setiawan, Stephan Peitz, Yova Kementchedjhieva,
• Abstract summary: Subword tokenization requires balancing computational efficiency and vocabulary coverage.<n>We propose a vocabulary-free encoder that generates input embeddings from text rendered as pixels.
• Score: 9.753745943931207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Subword tokenization requires balancing computational efficiency and vocabulary coverage, which often leads to suboptimal performance on languages and scripts not prioritized during training. We propose to augment pretrained language models with a vocabulary-free encoder that generates input embeddings from text rendered as pixels. Through experiments on English-centric language models, we demonstrate that our approach substantially improves machine translation performance and facilitates effective cross-lingual transfer, outperforming tokenizer-based methods. Furthermore, we find that pixel-based representations outperform byte-level approaches and standard vocabulary expansion. Our approach enhances the multilingual capabilities of monolingual language models without extensive retraining and reduces decoding latency via input compression.

Related papers

• Scaling LLM Pre-training with Vocabulary Curriculum [0.0]
  We introduce vocabulary curriculum learning, an approach that improves pretraining efficiency with log-linear scaling gains relative to vocabulary size.<n>Our method alternates between entropy-guided vocabulary expansion and model optimization, enabling models to learn transferable representations across diverse tokenization granularities.<n>Experiments on small-scale GPT models demonstrate improved scaling efficiency, reinforcing the effectiveness of dynamic tokenization.
  arXiv  Detail & Related papers  (2025-02-25T07:18:29Z)
• Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling [10.985444895887207]
  We introduce Over-Tokenized Transformers, a framework that decouples input and output vocabularies to improve language modeling performance. We uncover a log-linear relationship between input vocabulary size and training loss, demonstrating that larger input vocabularies consistently enhance model performance. Our findings highlight the importance of tokenization in scaling laws and provide practical insight for tokenizer design.
  arXiv  Detail & Related papers  (2025-01-28T14:15:42Z)
• Self-Translate-Train: Enhancing Cross-Lingual Transfer of Large Language Models via Inherent Capability [31.025371443719404]
  Self-Translate-Train is a method that lets large language models translate training data into the target language and fine-tunes the model on its own generated data. By demonstrating that Self-Translate-Train outperforms zero-shot transfer, we encourage further exploration of better methods to elicit cross-lingual capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-06-29T14:40:23Z)
• No Train but Gain: Language Arithmetic for training-free Language Adapters enhancement [59.37775534633868]
  We introduce a novel method called language arithmetic, which enables training-free post-processing. The effectiveness of the proposed solution is demonstrated on three downstream tasks in a MAD-X-based set of cross-lingual schemes.
  arXiv  Detail & Related papers  (2024-04-24T08:52:40Z)
• Accelerating Multilingual Language Model for Excessively Tokenized Languages [3.5570874721859016]
  tokenizers in large language models (LLMs) often fragment a text into character or Unicode-level tokens in non-Roman alphabetic languages. We introduce a simple yet effective framework to accelerate text generation in such languages.
  arXiv  Detail & Related papers  (2024-01-19T12:26:57Z)
• 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)
• Lifting the Curse of Multilinguality by Pre-training Modular Transformers [72.46919537293068]
  multilingual pre-trained models suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We introduce language-specific modules, which allows us to grow the total capacity of the model, while keeping the total number of trainable parameters per language constant. Our approach enables adding languages post-hoc with no measurable drop in performance, no longer limiting the model usage to the set of pre-trained languages.
  arXiv  Detail & Related papers  (2022-05-12T17:59:56Z)
• Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution [16.939016405962526]
  We propose a straightforward vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models. Our approach is suitable for large-scale datasets, applies to distant languages with unseen scripts and incurs only minor degradation on the translation performance for the original language pairs.
  arXiv  Detail & Related papers  (2021-03-11T17:10:21Z)
• UNKs Everywhere: Adapting Multilingual Language Models to New Scripts [103.79021395138423]
  Massively multilingual language models such as multilingual BERT (mBERT) and XLM-R offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. Due to their limited capacity and large differences in pretraining data, there is a profound performance gap between resource-rich and resource-poor target languages. We propose novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts.
  arXiv  Detail & Related papers  (2020-12-31T11:37:28Z)
• Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision [110.66085917826648]
  We develop a technique that extrapolates multimodal alignments to language-only data by contextually mapping language tokens to their related images. "vokenization" is trained on relatively small image captioning datasets and we then apply it to generate vokens for large language corpora. Trained with these contextually generated vokens, our visually-supervised language models show consistent improvements over self-supervised alternatives on multiple pure-language tasks.
  arXiv  Detail & Related papers  (2020-10-14T02:11:51Z)
• On the Importance of Word Order Information in Cross-lingual Sequence Labeling [80.65425412067464]
  Cross-lingual models that fit into the word order of the source language might fail to handle target languages. We investigate whether making models insensitive to the word order of the source language can improve the adaptation performance in target languages.
  arXiv  Detail & Related papers  (2020-01-30T03:35:44Z)

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.