Context Augmentation

Context augmentation enhances machine learning models by enriching input data with relevant supplementary information, improving performance on tasks where limited or ambiguous context hinders accuracy. Current research focuses on leveraging large language models (LLMs) to generate this augmented context, employing techniques like retrieval-augmented generation (RAG) and synthetic data creation to address data scarcity and improve model robustness across diverse applications, including entity linking, question answering, and grammatical error correction. These advancements significantly impact various fields by improving the accuracy and reliability of AI systems in tasks requiring nuanced understanding of context, leading to more effective and efficient solutions.