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LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization (2401.06034v6)

Published 11 Jan 2024 in cs.CL

Abstract: Pretrained LLMs (PLMs) have become remarkably adept at task and language generalization. Nonetheless, they often fail when faced with unseen languages. In this work, we present LinguAlchemy, a regularization method that incorporates various linguistic information covering typological, geographical, and phylogenetic features to align PLMs representation to the corresponding linguistic information on each language. Our LinguAlchemy significantly improves the performance of mBERT and XLM-R on low-resource languages in multiple downstream tasks such as intent classification, news classification, and semantic relatedness compared to fully finetuned models and displaying a high degree of unseen language generalization. We further introduce AlchemyScale and AlchemyTune, extension of LinguAlchemy which adjusts the linguistic regularization weights automatically, alleviating the need for hyperparameter search.

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Citations (4)
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Summary

  • The paper introduces LinguAlchemy, a method that integrates typological, geographical, and phylogenetic data to enhance language model performance on unseen languages.
  • It demonstrates that incorporating these linguistic features into models like mBERT and XLM-R raises accuracy by approximately 18% and 2%, respectively, compared to traditional adapter models.
  • It outlines scalable extensions, AlchemyScale and AlchemyTune, which streamline hyperparameter tuning and training efficiency, though with a noted trade-off in seen language accuracy.

Introduction to LinguAlchemy

Pretrained LLMs (PLMs) have dramatically altered the landscape of NLP. Despite their advanced capabilities, these models struggle with generalizing to languages they have not been explicitly trained on. Addressing this challenge is crucial for creating equitable language technology. In this context, a groundbreaking methodology nicknamed LinguAlchemy has been presented. Through the fusion of linguistic features, this technique has been crafted to bolster PLMs' performance across a diverse collection of languages that they haven't encountered during training – thereby showcasing its strength in inclusivity and accessibility in language processing technology.

Enhancing Unseen Language Performance

LinguAlchemy stands out by integrating linguistic knowledge from typological, geographical, and phylogenetic data into LLMs like mBERT and XLM-R, empowering them with the ability to recognize and process languages they have never seen. For example, this technique has led to approximately an 18% increase in mBERT's accuracy for unseen languages and about a 2% hike for XLM-R. The method departs from traditional adapter models that require language-specific modules and instead relies on a shared knowledge base among languages. Consequently, this enables LLMs to carry out inference tasks without needing prior identification of the language, a significant step toward more seamless and inclusive multilingual language processing.

Dynamic and Scalable Approach

In pursuit of efficiency and effectiveness, the researchers have developed two extensions: AlchemyScale and AlchemyTune. The former is a dynamic scaling method for finely tuning classification and auxiliary loss factors, while the latter conceptualizes these factors as trainable parameters within the model's architecture. These innovative approaches alleviate the challenge of hyperparameter search, a common obstacle in optimizing machine learning models, thus simplifying and speeding up the training process.

Robust Evaluation and Implications

The proposed system underwent rigorous testing on the MASSIVE dataset, which featured a multitude of languages with various linguistic attributes. The sweeping improvements in LLM performance for unseen languages underscore the significance of this approach. While it has shown promise, there's an observed trade-off where seen language accuracy diminishes as unseen language performance escalates. This finding prompts the need for continuous refinement of the method to achieve a balanced enhancement across all language representations. Despite the room for further improvement, LinguAlchemy has set a new standard for cross-lingual generalization and the development of responsive and inclusive LLMs.

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