While the observed improvements in the transition of responses from ‘poor’ to ‘good’ (with one such example in each LLM-Chatbot) may not be significant, they underline the present capacity of LLMs to acknowledge potential inaccuracies when prompted and make attempts at self-correction ( Table 5 , Table 6 , Table 7 ).
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Benchmarking large language models' performances for myopia care: a comparative analysis of ChatGPT-3.5, ChatGPT-4.0, and Google Bard.
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