  Given varying prompts regarding a factoid question, can a large language
model (LLM) reliably generate factually correct answers? Existing LLMs may
generate distinct responses for different prompts. In this paper, we study the
problem of quantifying knowledge contained in an LLM regarding a given set of
facts. We propose KaRR, a statistical approach to assess factual knowledge for
LLMs. The main idea is to estimate the ratio of LLM generating text
corresponding to the answer entity given diverse prompts of the subject and the
querying relation, versus it generating by random chances. Our assessment suite
contains a comprehensive set of 994,123 entities and 600 relations, with
1,395,905 text aliases. We use our method to evaluate 20 LLMs of various sizes,
including LLaMA, Alpaca, OPT, etc. Experiments show that our results have a
strong correlation (0.43 Kendall's $\tau$) with the results of human assessment
on LLMs. Our results reveal that the knowledge in LLMs with the same backbone
architecture adheres to the scaling law, while tuning on instruction-following
data sometimes compromises the model's capability to generate factually correct
text reliably.

Computation and Language;Machine Learning