Semantic Textual Similarity

Semantic textual similarity (STS) focuses on computationally measuring the semantic similarity between pairs of sentences, a crucial task in natural language processing with applications in information retrieval, question answering, and other areas. Current research emphasizes improving the accuracy and efficiency of STS models, exploring techniques like contrastive learning, regression frameworks, and the integration of large language models (LLMs) and graph-based methods to capture nuanced semantic relationships and handle diverse data types. Advances in STS have significant implications for various fields, enabling more effective search engines, improved machine translation, and enhanced analysis of complex textual data in domains like law and medicine.