Customizing GPT Models for Domain-Specific NLP Tasks: Techniques for Fine-tuning and Adaptation
In the rapidly evolving field of Natural Language Processing (NLP), pre-trained models such as GPT have demonstrated remarkable capabilities across a variety of tasks.
However, out-of-the-box usage of these models often falls short when addressing specific domain-related tasks that require understanding and generating specialized language.
This presentation, titled “Customizing GPT Models for Domain-Specific NLP Tasks: Techniques for Fine-tuning and Adaptation”, aims to tackle this challenge.
We’ll explore strategies for adapting GPT models to specific domains, highlighting methods of fine-tuning on domain-specific data to improve model performance. Detailed case studies will be shared, where these techniques have led to notable improvements in areas such as legal document understanding, medical text processing, and more.
This talk is intended for anyone looking to leverage the power of GPT and other transformer-based models in a specialized field, offering guidance on how to achieve superior results by customizing these models for specific domains.
Data Science Lead at Boomi
Swagata is a Data Scientist with 6+years of experience who meandered through the world of technology, business, design while doing Masters from CMU to finally find her calling in the world of data analytics, ML and AI. A trained classical dancer, passionate guitarist, love to live life to the fullest everyday.