Advancing Quality Assessment in Vertical Field: Scoring Calculation for Text Inputs to Large Language Models

Abstract

With the advent of Transformer-based generative AI, there has been a surge in research focused on large-scale generative language models, especially in natural language processing applications. Moreover, these models have demonstrated immense potential across various vertical fields, ranging from education and history to mathematics, medicine, information processing, and cybersecurity. In research on AI applications in Chinese, it has been found that the quality of text generated by generative AI has become a central focus of attention. However, research on the quality of input text still remains an overlooked priority. Consequently, based on the vectorization comparison of vertical field lexicons and text structure analysis, proposes three input indicators D1, D2, and D3 that affect the quality of generation. Based on this, we studied a text quality evaluation algorithm called VFS (Vertical Field Score) and designed an output evaluation metric named V-L (Vertical-Length). Our experiments indicate that higher-scoring input texts enable generative AI to produce more effective outputs. This enhancement aids users, particularly in leveraging generative AI for question-answering in specific vertical fields, thereby improving response effectiveness and accuracy.