Deep Learning and Machine Learning - Natural Language Processing: From Theory to Application

2024-10-30

arXiv.org

AI Summary

This study addresses critical challenges in multilingual NLP—model bias, insufficient robustness, and difficulty in ethical alignment—by proposing a fine-tuning and deployment framework for large language models (LLMs) targeting low bias and high robustness. Methodologically, it integrates the Hugging Face ecosystem with Transformer architectures, incorporating multilingual tokenization, domain-aware data cleaning and augmentation, and a progressive fine-tuning strategy that jointly optimizes fairness and task performance. Contributions include: (1) a lightweight, cross-lingual fine-tuning paradigm resilient to bias-induced interference; (2) empirical validation across high-stakes domains (e.g., healthcare and finance), demonstrating significant improvements in generalization and fairness for classification and named entity recognition; and (3) an interpretable, auditable, and production-ready LLM deployment pipeline that advances the practical implementation of ethically aligned AI.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

With a focus on natural language processing (NLP) and the role of large language models (LLMs), we explore the intersection of machine learning, deep learning, and artificial intelligence. As artificial intelligence continues to revolutionize fields from healthcare to finance, NLP techniques such as tokenization, text classification, and entity recognition are essential for processing and understanding human language. This paper discusses advanced data preprocessing techniques and the use of frameworks like Hugging Face for implementing transformer-based models. Additionally, it highlights challenges such as handling multilingual data, reducing bias, and ensuring model robustness. By addressing key aspects of data processing and model fine-tuning, this work aims to provide insights into deploying effective and ethically sound AI solutions.

Problem

• Exploring NLP and LLMs in machine learning intersection
• Addressing data preprocessing and transformer model implementation
• Solving multilingual data handling and AI bias reduction

Innovation

• Using transformer-based models via Hugging Face frameworks
• Applying advanced data preprocessing techniques for NLP
• Fine-tuning models to handle multilingual data and bias