Large Language Models (LLMs) have shown great potential in Natural Language
Processing (NLP) tasks. However, recent literature reveals that LLMs generate
nonfactual responses intermittently, which impedes the LLMs' reliability for
further utilization. In this paper, we propose a novel self-detection method to
detect which questions that a LLM does not know that are prone to generate
nonfactual results. Specifically, we first diversify the textual expressions
for a given question and collect the corresponding answers. Then we examine the
divergencies between the generated answers to identify the questions that the
model may generate falsehoods. All of the above steps can be accomplished by
prompting the LLMs themselves without referring to any other external
resources. We conduct comprehensive experiments and demonstrate the
effectiveness of our method on recently released LLMs, e.g., Vicuna, ChatGPT,
and GPT-4.

提出了一种新颖的自我检测方法，通过扩展问题的文本表达并收集相应的答案，检测大型语言模型（LLMs）是否会产生虚假回答，证明了该方法在 LLM 效果上的有效性。