We're Calling an Intervention: Exploring the Fundamental Hurdles in Adapting Language Models to Nonstandard Text

• URL: http://arxiv.org/abs/2404.07304v2
• Date: Sun, 16 Jun 2024 02:09:44 GMT
• Title: We're Calling an Intervention: Exploring the Fundamental Hurdles in Adapting Language Models to Nonstandard Text
• Authors: Aarohi Srivastava, David Chiang,
• Abstract summary: We present a suite of experiments that allow us to understand the underlying challenges of language model adaptation to nonstandard text. We do so by designing interventions that approximate several types of linguistic variation and their interactions with existing biases of language models. Applying our interventions during language model adaptation with varying size and nature of training data, we gain important insights into when knowledge transfer can be successful.
• Score: 8.956635443376527
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a suite of experiments that allow us to understand the underlying challenges of language model adaptation to nonstandard text. We do so by designing interventions that approximate several types of linguistic variation and their interactions with existing biases of language models. Applying our interventions during language model adaptation with varying size and nature of training data, we gain important insights into when knowledge transfer can be successful, as well as the aspects of linguistic variation that are particularly difficult for language models to deal with. For instance, on text with character-level variation, performance improves with even a few training examples but approaches a plateau, suggesting that more data is not the solution. In contrast, on text with variation involving new words or meanings, far more data is needed, but it leads to a massive breakthrough in performance. Our findings reveal that existing models lack the necessary infrastructure to handle diverse forms of nonstandard text and linguistic variation, guiding the development of more resilient language modeling techniques for the future. We make the code for our interventions, which can be applied to any English text data, publicly available.

Related papers

• Harnessing the Intrinsic Knowledge of Pretrained Language Models for Challenging Text Classification Settings [5.257719744958367]
  This thesis explores three challenging settings in text classification by leveraging the intrinsic knowledge of pretrained language models (PLMs) We develop models that utilize features based on contextualized word representations from PLMs, achieving performance that rivals or surpasses human accuracy. Lastly, we tackle the sensitivity of large language models to in-context learning prompts by selecting effective demonstrations.
  arXiv  Detail & Related papers  (2024-08-28T09:07:30Z)
• Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution [7.681258910515419]
  Tabular data presents unique challenges due to its heterogeneous nature and complex structural relationships. High predictive performance and robustness in tabular data analysis holds significant promise for numerous applications. The recent advent of large language models, such as GPT and LLaMA, has further revolutionized the field, facilitating more advanced and diverse applications with minimal fine-tuning.
  arXiv  Detail & Related papers  (2024-08-20T04:59:19Z)
• Variationist: Exploring Multifaceted Variation and Bias in Written Language Data [3.666781404469562]
  Exploring and understanding language data is a fundamental stage in all areas dealing with human language. Yet, there is currently a lack of a unified, customizable tool to seamlessly inspect and visualize language variation and bias. In this paper, we introduce Variationist, a highly-modular, descriptive, and task-agnostic tool that fills this gap.
  arXiv  Detail & Related papers  (2024-06-25T15:41:07Z)
• Exploring Tokenization Strategies and Vocabulary Sizes for Enhanced Arabic Language Models [0.0]
  This paper examines the impact of tokenization strategies and vocabulary sizes on the performance of Arabic language models. Our study uncovers limited impacts of vocabulary size on model performance while keeping the model size unchanged. Paper's recommendations include refining tokenization strategies to address dialect challenges, enhancing model robustness across diverse linguistic contexts, and expanding datasets to encompass the rich dialect based Arabic.
  arXiv  Detail & Related papers  (2024-03-17T07:44:44Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Towards Zero-shot Language Modeling [90.80124496312274]
  We construct a neural model that is inductively biased towards learning human languages. We infer this distribution from a sample of typologically diverse training languages. We harness additional language-specific side information as distant supervision for held-out languages.
  arXiv  Detail & Related papers  (2021-08-06T23:49:18Z)
• Specializing Multilingual Language Models: An Empirical Study [50.7526245872855]
  Contextualized word representations from pretrained multilingual language models have become the de facto standard for addressing natural language tasks. For languages rarely or never seen by these models, directly using such models often results in suboptimal representation or use of data.
  arXiv  Detail & Related papers  (2021-06-16T18:13:55Z)
• Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages [81.90356787324481]
  Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected.
  arXiv  Detail & Related papers  (2020-10-20T17:31:07Z)
• Grounded Compositional Outputs for Adaptive Language Modeling [59.02706635250856]
  A language model's vocabulary$-$typically selected before training and permanently fixed later$-$affects its size. We propose a fully compositional output embedding layer for language models. To our knowledge, the result is the first word-level language model with a size that does not depend on the training vocabulary.
  arXiv  Detail & Related papers  (2020-09-24T07:21:14Z)
• Data Augmentation for Spoken Language Understanding via Pretrained Language Models [113.56329266325902]
  Training of spoken language understanding (SLU) models often faces the problem of data scarcity. We put forward a data augmentation method using pretrained language models to boost the variability and accuracy of generated utterances.
  arXiv  Detail & Related papers  (2020-04-29T04:07:12Z)

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.