Download PDFOpen PDF in browser

Enhancing Accuracy with Transfer Learning and Domain-Specific Language Models

EasyChair Preprint no. 13062

15 pagesDate: April 21, 2024


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

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Dylan Stilinki and Kaledio Potter},
  title = {Enhancing Accuracy with Transfer Learning and Domain-Specific Language Models},
  howpublished = {EasyChair Preprint no. 13062},

  year = {EasyChair, 2024}}
Download PDFOpen PDF in browser