Context Tuning

Context tuning enhances the performance of large language models (LLMs) by leveraging in-context examples during inference, rather than relying solely on extensive pre-training or fine-tuning. Current research focuses on adapting this technique to various modalities (text, image, etc.), improving retrieval of relevant contextual information, and developing efficient methods for incorporating knowledge and multiple iterative learning steps. This approach offers significant advantages by reducing the need for large training datasets and enabling faster adaptation to new tasks, impacting both the efficiency of LLM development and their applicability to resource-constrained environments.