Comparing Google Gemini and ChatGPT performance using ROC curves is not straightforward as they are primarily generative large language models, not traditional binary classifiers. To generate an ROC curve, these models must first be adapted for classification tasks, often by prompting them to output binary decisions or a confidence score regarding a specific class. The main challenge lies in extracting probabilistic or continuous scores
from their text-based outputs, which are typically required for plotting a robust ROC curve and calculating metrics like AUC (Area Under the Curve). Therefore, any direct performance comparison via ROC curves would heavily depend on the specific classification task, the quality of prompt engineering, and the method used to quantify their confidence or likelihood
for each prediction. Absent standardized benchmarks for raw LLM classification capabilities, evaluations typically show task-specific strengths where one model might slightly outperform the other based on training data and architectural nuances. More details: https://www.kerg-ufa.ru/component/content/article/16-news-kerg-ufa/271-v-novyj-god-na-novom-volkswagen.html?Itemid=101&tmpl=component&return_url=https://infoguide.com.ua/