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In the current era, a multitude of language models has emerged to cater touser inquiries. Notably, the GPT-3.5 Turbo language model has gainedsubstantial attention as the underlying technology for ChatGPT. Leveragingextensive parameters, this model adeptly responds to a wide range of questions.However, due to its reliance on internal knowledge, the accuracy of responsesmay not be absolute. This article scrutinizes ChatGPT as a Question AnsweringSystem (QAS), comparing its performance to other existing QASs. The primaryfocus is on evaluating ChatGPT's proficiency in extracting responses fromprovided paragraphs, a core QAS capability. Additionally, performancecomparisons are made in scenarios without a surrounding passage. Multipleexperiments, exploring response hallucination and considering questioncomplexity, were conducted on ChatGPT. Evaluation employed well-known QuestionAnswering (QA) datasets, including SQuAD, NewsQA, and PersianQuAD, acrossEnglish and Persian languages. Metrics such as F-score, exact match, andaccuracy were employed in the assessment. The study reveals that, while ChatGPTdemonstrates competence as a generative model, it is less effective in questionanswering compared to task-specific models. Providing context improves itsperformance, and prompt engineering enhances precision, particularly forquestions lacking explicit answers in provided paragraphs. ChatGPT excels atsimpler factual questions compared to "how" and "why" question types. Theevaluation highlights occurrences of hallucinations, where ChatGPT providesresponses to questions without available answers in the provided context.

Evaluating ChatGPT as a Question Answering System: A Comprehensive Analysis and Comparison with Existing Models