Tokenization is Sensitive to Language Variation

• URL: http://arxiv.org/abs/2502.15343v1
• Date: Fri, 21 Feb 2025 09:58:54 GMT
• Title: Tokenization is Sensitive to Language Variation
• Authors: Anna Wegmann, Dong Nguyen, David Jurgens,
• Abstract summary: Tokenizers split texts into smaller units and might behave differently for less common linguistic forms.<n>This might affect downstream LLM performance differently on two types of tasks.<n>We investigate how key algorithmic design choices impact downstream models' performances.
• Score: 14.568179478275255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Variation in language is ubiquitous and often systematically linked to regional, social, and contextual factors. Tokenizers split texts into smaller units and might behave differently for less common linguistic forms. This might affect downstream LLM performance differently on two types of tasks: Tasks where the model should be robust to language variation (e.g., for semantic tasks like NLI, labels do not depend on whether a text uses British or American spelling) and tasks where the model should be sensitive to language variation (e.g., for form-based tasks like authorship verification, labels depend on whether a text uses British or American spelling). We pre-train BERT base models for the popular Byte-Pair Encoding algorithm to investigate how key algorithmic design choices impact downstream models' performances: fitting corpus, pre-tokenizer and vocabulary size. We find that the best tokenizer varies on the two task types -- with the pre-tokenizer having the biggest impact on performance. Further, we introduce a new approach to estimate tokenizer impact on downstream LLM performance, showing significant improvement over techniques like R\'enyi efficiency. We encourage more work on language variation and its relation to tokenizers and thus LLM performance.

Related papers

• Retrofitting Large Language Models with Dynamic Tokenization [3.608780819053423]
  We propose retrofitting current language models with dynamic tokenization. We merge frequent subword sequences in a batch, then apply a pre-trained embedding-prediction hypernetwork to compute the token embeddings on-the-fly. We find that dynamic tokenization can mitigate the limitations of static tokenization by substantially improving inference speed and promoting fairness across languages.
  arXiv  Detail & Related papers  (2024-11-27T17:51:58Z)
• Language Models for Text Classification: Is In-Context Learning Enough? [54.869097980761595]
  Recent foundational language models have shown state-of-the-art performance in many NLP tasks in zero- and few-shot settings. An advantage of these models over more standard approaches is the ability to understand instructions written in natural language (prompts) This makes them suitable for addressing text classification problems for domains with limited amounts of annotated instances.
  arXiv  Detail & Related papers  (2024-03-26T12:47:39Z)
• Identifying and Analyzing Task-Encoding Tokens in Large Language Models [55.03191279766383]
  In this paper, we identify and analyze task-encoding tokens on whose representations the task performance depends. We show that template and stopword tokens are the most prone to be task-encoding. Our work sheds light on how large language models (LLMs) learn to perform a task from demonstrations, deepens our understanding of the varied roles different types of tokens play in LLMs, and provides insights for avoiding instability from improperly utilizing task-encoding tokens.
  arXiv  Detail & Related papers  (2024-01-20T20:55:21Z)
• Does Manipulating Tokenization Aid Cross-Lingual Transfer? A Study on POS Tagging for Non-Standardized Languages [18.210880703295253]
  We finetune pretrained language models (PLMs) on seven languages from three different families. We analyze their zero-shot performance on closely related, non-standardized varieties. Overall, we find that the similarity between the percentage of words that get split into subwords in the source and target data is the strongest predictor for model performance on target data.
  arXiv  Detail & Related papers  (2023-04-20T08:32:34Z)
• A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning [8.052271364177988]
  Subword tokenization is a commonly used input pre-processing step in most recent NLP models. We propose a vocabulary-free neural tokenizer by distilling segmentation information from subword tokenization. Our tokenizer consistently improves performance on multilingual (NLI) and code-switching (sentiment analysis) tasks.
  arXiv  Detail & Related papers  (2022-04-22T16:50:49Z)
• Impact of Tokenization on Language Models: An Analysis for Turkish [2.4660652494309936]
  We train tokenizers and pretrain medium-sized language models using RoBERTa pretraining procedure on the Turkish split of the OSCAR corpus. Our experiments, supported by statistical tests, reveal that Morphological-level tokenizer has challenging performance with de facto tokenizers. We find that increasing the vocabulary size improves the performance of Morphological and Word-level tokenizers more than that of de facto tokenizers.
  arXiv  Detail & Related papers  (2022-04-19T12:01:46Z)
• To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP [0.0]
  We investigate three categories of text augmentation methodologies which perform changes on the syntax. We compare them on part-of-speech tagging, dependency parsing and semantic role labeling for a diverse set of language families. Our results suggest that the augmentation techniques can further improve over strong baselines based on mBERT.
  arXiv  Detail & Related papers  (2021-11-18T10:52:48Z)
• 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)
• FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding [85.29270319872597]
  We propose an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. To tackle this issue, we propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language.
  arXiv  Detail & Related papers  (2020-09-10T22:42:15Z)
• Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification [52.69730591919885]
  We present a semi-supervised adversarial training process that minimizes the maximal loss for label-preserving input perturbations. We observe significant gains in effectiveness on document and intent classification for a diverse set of languages.
  arXiv  Detail & Related papers  (2020-07-29T19:38:35Z)
• 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.