Large Language Models Driven Health Text Information Analysis in Consumer Electronics

With the increasing awareness of consumer health and the arrival of the big data era, how to efficiently extract valuable knowledge from massive health text information has become an urgent problem. Large Language Models (LLMs) provide a new approach for intelligent processing of consumer health information with its powerful natural language processing. This article proposes a method for LLMs-driven health text information analysis in consumer electronics. It demonstrates how LLMs effectively analyze consumer health text information and improve the accuracy of health information generation. Based on LLMs, the proposed method first establishes a health text information component recognition model (HTICRM), which aims to identify the location and type of key information input into consumer health text. Then when there are diverse positions of text information components that cannot be accurately analyzed, this article uses LLMs reflection and P-Tuning v2 to generate health text information components to narrow the feature gap between the generated data and general consumer health domain data. Finally, the experimental results verify that the proposed method can help to promote the further application and development of LLMs in the field of consumer health, providing consumers with more scientific and accurate health guidance.