Context Sensitive

Context-sensitive processing aims to improve the performance of language models by incorporating contextual information to enhance understanding and generation. Current research focuses on integrating diverse contextual signals (e.g., web search results, large language model outputs) into existing architectures like Transformers and developing efficient algorithms for grammar-constrained decoding, particularly for structured output tasks such as code generation and parsing. These advancements lead to more accurate and robust language models with improved performance on various NLP tasks, impacting fields like information retrieval, logical reasoning, and program synthesis.