Evaluating Autoformalization Robustness via Semantically Similar Paraphrasing 事件

Evaluating Autoformalization Robustness via Semantically Similar Paraphrasing arXiv:2511.12784v3 Announce Type: replace Abstract: Large Language Models (LLMs) have recently emerged as powerful tools for autoformalization. Despite their impressive performance, these models can still struggle to produce grounded and verifiable formalizations. Recent work in text-to-SQL, has revealed that LLMs can be sensitive to paraphrased natural language (NL) inputs, even when high degrees of semantic fidelity