Enhancing Accuracy with Transfer Learning and Domain-Specific Language Models

EasyChair Preprint 13062

Abstract

In recent years, the field of natural language processing (NLP) has witnessed significant advancements, largely attributed to the advent of transfer learning and domain-specific language models (LM). Transfer learning, a technique wherein a model trained on a large dataset is fine-tuned on a specific task or domain, has proven to be highly effective in improving the performance of NLP systems, especially when data is limited. Additionally, domain-specific language models, tailored to particular domains or industries, have shown remarkable success in capturing domain-specific nuances and improving accuracy.

This paper explores the synergistic benefits of combining transfer learning and domain-specific language models to enhance accuracy in NLP tasks. Firstly, it delves into the principles behind transfer learning, highlighting its ability to leverage pre-trained models and adapt them to new tasks with minimal data requirements. Next, it discusses the importance of domain-specific language models in capturing specialized vocabulary, syntax, and semantics characteristic of specific domains, thereby enhancing model performance in domain-specific tasks.

Furthermore, the paper presents case studies and empirical evidence showcasing the efficacy of incorporating domain-specific language models in transfer learning frameworks across various domains, including finance, healthcare, legal, and customer service. It demonstrates how fine-tuning pre-trained language models on domain-specific corpora leads to significant improvements in accuracy, outperforming generic models trained on broad datasets.

Keyphrases: Accuracy Enhancement, Challenges, Domain Adaptation, Domain-specific Language Models, Future research opportunities, Natural Language Processing (NLP), Synergistic benefits, Transfer Learning, case studies, data preprocessing, empirical evidence, fine-tuning, model selection, pre-trained models

@booklet{EasyChair:13062,
  author    = {Dylan Stilinki and Kaledio Potter},
  title     = {Enhancing Accuracy with Transfer Learning and Domain-Specific Language Models},
  howpublished = {EasyChair Preprint 13062},
  year      = {EasyChair, 2024}}