Qtok: A Comprehensive Framework for Evaluating Multilingual Tokenizer Quality in Large Language Models

• URL: http://arxiv.org/abs/2410.12989v1
• Date: Wed, 16 Oct 2024 19:34:34 GMT
• Title: Qtok: A Comprehensive Framework for Evaluating Multilingual Tokenizer Quality in Large Language Models
• Authors: Iaroslav Chelombitko, Egor Safronov, Aleksey Komissarov,
• Abstract summary: The quality of tokenization can significantly impact a model's ability to handle diverse languages effectively. We introduce Qtok, a tool designed to assess tokenizer quality with a specific emphasis on their performance in multilingual contexts. Qtok applies these metrics to evaluate 13 distinct tokenizers from 58 publicly available models, analyzing their output across different linguistic contexts.
• Score: 0.0
• License:
• Abstract: In the development of Large Language Models (LLMs), considerable attention has been given to the quality of training datasets. However, the role of tokenizers in the LLM training pipeline, particularly for multilingual models, has received less focus. The quality of tokenization can significantly impact a model's ability to handle diverse languages effectively. We introduce Qtok, a tool designed to assess tokenizer quality with a specific emphasis on their performance in multilingual contexts. Our research proposes a set of metrics for evaluating tokenizer quality, including measures of language coverage, token completeness, and distribution across languages and linguistic categories. Qtok applies these metrics to evaluate 13 distinct tokenizers from 58 publicly available models, analyzing their output across different linguistic contexts. Our analysis revealed significant variations in token distribution across languages and categories, highlighting potential biases and areas for improvement in current tokenization strategies. This research contributes to the field of tokenizer evaluation within multilingual LLM development by providing a systematic approach to assessing tokenizer quality. Our findings highlight the critical role of tokenization in multilingual LLM capability. The Qtok tool and our analysis methodology offer practical means for researchers to evaluate and improve tokenization strategies for multilingual applications. We offer a method to compare tokenizer quality across these metrics, which may be useful when selecting or adjusting tokenizers for specific multilingual LLM applications.

Related papers

• How Does Quantization Affect Multilingual LLMs? [50.867324914368524]
  Quantization techniques are widely used to improve inference speed and deployment of large language models. We conduct a thorough analysis of quantized multilingual LLMs, focusing on performance across languages and at varying scales.
  arXiv  Detail & Related papers  (2024-07-03T15:39:40Z)
• Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking [1.3716808114696444]
  Large Language Models (LLMs) are becoming crucial across various fields, emphasizing the urgency for high-quality models in underrepresented languages. This study explores the unique challenges faced by low-resource languages, such as data scarcity, model selection, evaluation, and computational limitations.
  arXiv  Detail & Related papers  (2024-05-07T21:58:45Z)
• The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights [108.40766216456413]
  We propose a question alignment framework to bridge the gap between large language models' English and non-English performance. Experiment results show it can boost multilingual performance across diverse reasoning scenarios, model families, and sizes. We analyze representation space, generated response and data scales, and reveal how question translation training strengthens language alignment within LLMs.
  arXiv  Detail & Related papers  (2024-05-02T14:49:50Z)
• DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models [76.79929883963275]
  DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems. It features a secure, user-friendly web interface for fine-grained human evaluation at both local utterance level and global dialogue level. Our evaluations reveal that while PLM fine-tuning leads to higher accuracy and coherence, LLM-based systems excel in producing diverse and likeable responses.
  arXiv  Detail & Related papers  (2024-01-04T11:27:48Z)
• On the Calibration of Multilingual Question Answering LLMs [57.296161186129545]
  We benchmark the calibration of several multilingual Large Language Models (MLLMs) on a variety of Question Answering tasks. We study different dimensions of calibration in in-distribution, out-of-distribution, and cross-lingual transfer settings. For decoder-only LLMs such as LlaMa2, we additionally find that in-context learning improves confidence calibration on multilingual data.
  arXiv  Detail & Related papers  (2023-11-15T03:29:02Z)
• Tokenizer Choice For LLM Training: Negligible or Crucial? [30.33170936148845]
  We study the influence of tokenizer choice on Large Language Models (LLMs) downstream performance by training 24 mono- and multilingual LLMs. We find that the tokenizer choice can significantly impact the model's downstream performance and training costs. We show that multilingual tokenizers trained on the five most frequent European languages require vocabulary size increases of factor three in comparison to English.
  arXiv  Detail & Related papers  (2023-10-12T22:44:19Z)
• Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model [15.839724725094916]
  Multi-level Multilingual Knowledge Distillation (MMKD) is a novel method for improving multilingual language models. We employ a teacher-student framework to adopt rich semantic representation knowledge in English BERT. We conduct experiments on cross-lingual evaluation benchmarks including XNLI, PAWS-X, and XQuAD.
  arXiv  Detail & Related papers  (2022-11-02T15:23:13Z)
• 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)
• XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization [128.37244072182506]
  Cross-lingual TRansfer Evaluation of Multilinguals XTREME is a benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models.
  arXiv  Detail & Related papers  (2020-03-24T19:09:37Z)

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.