“A Case Study of Fine-Tuning ChatGPT Models for Natural Language Processing with Deep Learning ”

Large pretrained language models like ChatGPT contain extensive knowledge about language but require adaptation for optimal performance on downstream natural language processing (NLP) tasks. This work provides a comprehensive case study fine-tuning ChatGPT, a leading conversational AI system created by Anthropic, for text classification, question answering, summarization, and grammatical error correction. Through controlled experiments, we evaluate prompt engineering, training schemes, model sizes, and regularization techniques for fine-tuning. Quantitative analysis on benchmark datasets combined with human evaluations reveal ChatGPT can be significantly improved through prompt optimization and fine-tuning on small domain-specific datasets. Our findings derive best practices for stable and effective fine-tuning of ChatGPT and similar foundation models to create specialized conversational agents for NLP. This applied research advances the methodology of adapting large models for targeted capabilities to responsibly unlock their potential

کلیدواژه ها:

natural language processing ، foundation models ، conversational AI ، fine-tuning ، transfer learning ، prompt engineering