3 accepted papers
Autoformalization, the task of translating informal mathematics into formal machine-verifiable languages, has long been challenging, even for individual statements. Beyond the statement level, mathematicians invest substantial effort in formalizing reusable abstractions such as common definitions an…
Code Large Language Models (Code LLMs) are being increasingly employed in real-life applications, so evaluating them is critical. While the conventional accuracy evaluates the performance of Code LLMs on a set of individual tasks, their self-consistency across different tasks is overlooked. Intuitiv…
Code Large Language Models (Code LLMs) have excelled at tasks like code completion but often miss deeper semantics such as execution effects and dynamic states. This paper aims to bridge the gap between Code LLMs' reliance on static text data and the need for semantic understanding for complex tasks…