Wang et al. [192] assessed the internal knowledge capabilities of several large models, namely
InstructGPT, ChatGPT-3.5, GPT-4, and BingChat [128], by examining their ability to answer open
questions based on the Natural Questions [91] and TriviaQA [81] datasets. The evaluation process
involved human assessment. The results of the study indicated that while GPT-4 and BingChat can
provide correct answers for more than 80% of the questions, there is still a remaining gap of over
15% to achieve complete accuracy. In the work of Honovich et al. [68], they conducted a review of
current factual consistency evaluation methods and highlighted the absence of a unified comparison
framework and the limited reference value of related scores compared to binary labels. To address
this, they transformed existing fact consistency tasks into binary labels, specifically considering
only whether there is a factual conflict with the input text, without factoring in external knowledge.
ASurveyonEvaluationofLargeLanguageModels