解析大模型在自然语言处理中的应用与挑战
Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application
- 结合Hugging Face框架实现Transformer模型
- 聚焦多语言处理与偏见减少等实际难题
- 适合关注AI落地与伦理的开发者参考
本文聚焦自然语言处理（NLP）与大语言模型（LLMs）的交叉领域，探讨机器学习、深度学习与人工智能的融合。随着人工智能在医疗、金融等领域的革新，分词、文本分类、实体识别等技术对理解人类语言至关重要。论文讨论了先进的数据预处理方法及Hugging Face等框架在基于Transformer模型实现中的应用。同时，强调了处理多语言数据、降低模型偏见和提升鲁棒性等挑战。通过优化数据处理与模型微调，旨在为部署高效且符合伦理的AI解决方案提供洞见。
原文摘要 · Abstract (English)
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.
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