
Large language models can embed factual knowledge, but reliably estimating the extent of this latent knowledge is challenging. This work proposes a novel in-context learning-based approach to estimate latent knowledge, which outperforms existing prompt-based methods in terms of reliability and performance.


coremsg

estimating-factual-knowledge-in-large-language-models-comparing-in-context-learning-and-prompting-based-approaches


Estimating Factual Knowledge in Large Language Models: Comparing In-Context Learning and Prompting-Based Approaches
