Control vectors are an advanced AI technique that straddles the domain between prompt engineering and model fine-tuning. They serve as intermediaries that allow developers to guide the behavior of a model without making permanent adjustments to its underlying structure. This is particularly useful in large language or generative models where direct manipulation of model weights (as in fine-tuning) is computationally expensive or undesirable. Control vectors can be implemented as additional input vectors that modify the activation pattern across the network, effectively steering the output generation towards desired attributes or styles specified by the control vector.
Historical overview: The concept of control vectors in AI emerged prominently in the mid-2010s as part of the broader exploration into more flexible model architectures. It gained traction as researchers sought ways to make models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) adaptable to specific tasks without extensive retraining.
Key contributors: While the development of control vectors as a distinct concept does not have a single inventor, it has been influenced by the work of researchers in neural network adaptability and transfer learning. Institutions like OpenAI and Google Brain have contributed to refining and popularizing the use of control vectors through their work on adaptable neural models.