and fairness across the 9 QA scenarios. Both GPT-3.5 and ChatGPT demonstrate significant advance-
ments compared to GPT-3 in their ability to answer general knowledge questions. In most domains,
ChatGPT surpasses GPT-3.5 by more than 2% in terms of performance [7, 150]. However, ChatGPT
performs slightly weaker than GPT-3.5 on the CommonsenseQA and Social IQA benchmarks.
This can be attributed to ChatGPT’s cautious nature, as it tends to decline to provide an answer
when there is insufficient information available. Fine-tuned models, such as Vícuna and ChatGPT,
exhibit exceptional performance with near-perfect scores, surpassing models that lack supervised
fine-tuning by a significant margin [4, 5]. Laskar et al. [95] evaluated the effectiveness of ChatGPT
on a range of academic datasets, including various tasks such as answering questions, summarizing
text, generating code, reasoning with commonsense, solving math problems, translating languages,
ASurveyonEvaluationofLargeLanguageModels