Frozen Language Model

Frozen language model research focuses on leveraging the power of pre-trained large language models (LLMs) without modifying their weights, aiming to improve efficiency and maintain the model's versatility across diverse tasks. Current research explores various techniques, including adapting LLMs with lightweight modules (like adapters or querying transformers) for multimodal capabilities (vision, speech) and specialized knowledge domains, or employing prompt engineering and retrieval-augmentation methods to guide the frozen model's behavior. This approach offers significant advantages in terms of reduced computational cost and improved adaptability to new tasks, impacting fields like medical diagnosis, speech recognition, and question answering by enabling efficient model deployment and adaptation in resource-constrained environments.