Merging Language and Domain Specific Models: The Impact on Technical Vocabulary Acquisition

TL;DR

This paper examines how merging general and domain-specific language models affects technical vocabulary understanding, revealing insights into knowledge transfer and model performance in multilingual NLP tasks.

Contribution

It introduces a detailed analysis of merging strategies for language models to improve technical vocabulary acquisition in multilingual settings.

Findings

Merged models show improved technical vocabulary comprehension.

Different merging methods vary in effectiveness.

Challenges in cross-lingual knowledge transfer are identified.

Abstract

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.