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Date of Award
2024
Document Type
Campus Access Master's Thesis
Degree Name
Master of Science in Health Informatics (MS)
Administrative Home Department
Department of Applied Computing
Advisor 1
Guy Hembroff
Committee Member 1
Rongua Xu
Committee Member 2
Paniz Hazaveh
Abstract
As the world is shifting towards digital platforms, it is important to make sure that available online information is accessible and comprehensible to audiences with limited literacy levels. The widespread use of LLMs makes it important to access their responses for simplification tasks especially when they are used for healthcare purposes to make sure that generated information is understandable to people with low literacy. In this research, responses generated by LLMs such as ChatGPT and Llama-2 are evaluated for simplification tasks using the Flesch Kincaid Grade scale. Instruction tuning is performed to evaluate the generation of simplified responses by LLMs according to reading grade level. Fine-tuning of Llama-2-7-B on a dataset containing the correct formula of the Flesch Kincaid grade scale is done using SFTTrainer. To maintain overall robustness, the fine-tuned model is merged with the base model, and evaluation is conducted. A calculator function is used for calculating the Flesch Kincaid reading grade level formula given the user input to guide the model for response text generation respective to the user’s grade level of understanding. Our research focused on the refinement of text simplification methodologies, warranting the response generated by LLMs, particularly related to healthcare, to become accessible and understandable to individuals as per their literacy levels.
Recommended Citation
Sifat Naseem, "ADVANCING HEALTH LITERACY THROUGH GENERATIVE AI: THE UTILIZATION OF OPEN-SOURCE LARGE LANGUAGE MODELS (LLMS) FOR TEXT SIMPLIFICATION AND READABILITY", Campus Access Master's Thesis, Michigan Technological University, 2024.