  This paper presents a framework that integrates Large Language Models (LLMs)
into translation validation, targeting LLVM compiler transformations where
formal verification tools fall short. Our framework first utilizes existing
formal verification tools for translation validation. In this work, we use
Alive2, a well-known tool in LLVM compiler verification, as an example. When
formal verification tools are unable to confirm a transformation's soundness,
our framework employs fine-tuned LLMs for prediction. It then applies fuzzing
to transformations predicted as potentially unsound by the LLMs due to return
values or memory inconsistencies, aiming to find counterexamples. In cases
where transformations are unsound for other reasons or sound, or if no
counterexamples emerge, the framework directly reports these outcomes without
further fuzzing. This methodology has shown effectiveness in complex
application such as deep-learning accelerator designs, where traditional formal
verification tools struggle.
