  Large Language Models (LLMs) have demonstrated impressive performance on
several benchmarks. However, traditional studies do not provide formal
guarantees on the performance of LLMs. In this work, we propose a novel
certification framework for LLM, QuaCer-C, wherein we formally certify the
knowledge-comprehension capabilities of popular LLMs. Our certificates are
quantitative - they consist of high-confidence, tight bounds on the probability
that the target LLM gives the correct answer on any relevant knowledge
comprehension prompt. Our certificates for the Llama, Vicuna, and Mistral LLMs
indicate that the knowledge comprehension capability improves with an increase
in the number of parameters and that the Mistral model is less performant than
the rest in this evaluation.
