融合通用与领域模型可提升技术词汇理解能力
Merging Language and Domain Specific Models: The Impact on Technical Vocabulary Acquisition
- 将通用语言模型与领域模型合并，增强技术术语理解
- 合并后模型在技术词汇任务上表现优于单一模型
- 适合跨语言技术文本处理的研究者参考
自然语言处理的进步催生了专业化语言模型，但在多语言环境下将领域知识融入通用模型仍具挑战，尤其体现在技术词汇的掌握上。本文研究了将通用语言模型与领域特定模型合并对技术词汇获取的影响，探讨了合并过程中知识迁移的机制。实验分析了合并过程对目标模型掌握专业术语能力的影响，并定量评估了合并模型的表现，与各组成部分模型进行对比。结果揭示了不同合并方法在增强领域知识方面的有效性，同时指出了在跨语言知识迁移中可能面临的挑战及未来方向。
原文摘要 · Abstract (English)
Advancements in Natural Language Processing have enabled specialized language models, but integrating domain-specific knowledge into general-purpose models in multilingual settings remains challenging, particularly for technical vocabulary. This paper investigates the integration of technical vocabulary in merged language models and explores the knowledge transfer mechanisms involved when combining a general-purpose language-specific model with a domain-specific model, focusing on the resulting model's comprehension of technical jargon. Our experiments analyze the impact of this merging process on the target model's proficiency in handling specialized terminology. We present a quantitative evaluation of the performance of the merged model, comparing it with that of the individual constituent models. The findings offer insights into the effectiveness of different model merging methods for enhancing domain-specific knowledge and highlight potential challenges and future directions in leveraging these methods for cross-lingual knowledge transfer in Natural Language Processing.
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