Achieving reliable communication has long been a fundamental challenge in networked systems. Semantic Error Correction (SEC) leverages the semantic understanding capabilities of language models (LMs) to perform application-layer error correction, complementing conventional channel decoding. While promising, existing SEC approaches rely solely on context captured by LMs at the application layer, ignoring the rich information available at the physical layer. To address this limitation, this paper introduces Cross-Layer SEC (CL-SEC), an LM-empowered error correction framework that integrates cross-layer information from both the physical and application layers to jointly correct corrupted words in text communication. Using a Bayesian combination in product form tailored to this framework, CL-SEC achieves significantly improved performance over methods that process information in isolated layers. CL-SEC shows substantial gains across multiple error-correction metrics, including bit-error rate, word-error rate, and semantic fidelity scores. Importantly, unlike most semantic communication systems that focus solely on recovering the semantic meaning of transmitted messages, CL-SEC aims to reconstruct the original transmitted message verbatim, leveraging the semantic understanding capabilities of LMs for precise reconstruction.

翻译：实现可靠通信一直是网络系统中的基本挑战。语义纠错（SEC）利用语言模型（LMs）的语义理解能力执行应用层纠错，作为传统信道解码的补充。尽管前景广阔，现有SEC方法仅依赖LMs在应用层捕获的上下文，忽略了物理层可用的丰富信息。为解决此局限，本文提出跨层语义纠错（CL-SEC），一种基于语言模型驱动的纠错框架，它整合物理层与应用层的跨层信息，联合纠正文本通信中的受损词汇。通过采用适用于该框架的乘积形式贝叶斯组合，CL-SEC实现了优于孤立层处理方法的性能提升。CL-SEC在比特错误率、词错误率和语义保真度分数等多个纠错指标上展现出显著优势。重要的是，与大多数仅关注恢复传输消息语义含义的语义通信系统不同，CL-SEC致力于逐字重建原始传输消息，利用语言模型的语义理解能力实现精确重建。