Context Distillation

Context distillation is a machine learning technique focused on improving model performance by integrating contextual information, such as instructions or examples, directly into the model's internal representations. Current research explores various applications, including enhancing language models for tasks like knowledge graph completion and multi-change captioning, and aligning language models to desired behaviors using reinforcement learning and contrastive methods. This approach offers significant potential for improving the efficiency and capabilities of various machine learning models, particularly in scenarios where access to large amounts of labeled data is limited, by effectively leveraging contextual information for improved generalization and performance.