Exploring Hybrid Intelligence for Topic Interpretation in Colorectal Cancer Research: A Comparative Study of GPT-3.5 and Human Expertise

Colorectal cancer is a widespread disease that significantly impacts the health of individuals worldwide. Understanding the needs and concerns of those affected by this disease is crucial for improving patient outcomes and enhancing the quality of care. Patient web forums have emerged as valuable platforms for individuals to openly share their...

Evaluating the effectiveness of large language models in meeting summarization with transcript segmentation techniques: How well does gpt-3.5-turbo perform on meeting summarization with topic and context-length window segmentation?

Large Language Models (LLM) have brought significant performance increase on many Natural Language Processing tasks. However LLMs have not been tested for meeting summarization. This research paper examines the effectiveness of the gpt-3.5-turbo model on the meeting summarization domain. However due to input length limitations, it cannot be...

How can large language models and prompt engineering be leveraged in Computer Science education?: Systematic literature review

In recent years, significant progress has been made in the field of natural language processing (NLP) through the development of large language models (LLMs) like BERT and ChatGPT. These models have showcased remarkable abilities across a range of NLP tasks. However, effectively harnessing their potential requires meticulous prompt engineering...