endotuning

Endotuning is a process in artificial intelligence, specifically within the domain of large language models (LLMs), that involves adjusting a pre-trained model's behavior without altering its core knowledge base. Unlike traditional fine-tuning, which modifies the model's weights through additional training on specific datasets, endotuning focuses on influencing the model's output through its input prompts or by manipulating its internal activations in a more subtle way.

One common approach to endotuning involves prompt engineering. This technique uses carefully crafted input prompts to guide the LLM towards generating desired responses. By including specific instructions, examples, or contextual information within the prompt, users can steer the model's behavior without retraining it. This is often referred to as in-context learning.

Another method of endotuning might involve influencing the model's internal state or attention mechanisms. This could be achieved through techniques that manipulate intermediate computations within the neural network. The goal is to achieve a specific outcome or characteristic in the model's output, such as increased creativity, reduced verbosity, or a more formal tone, without the computational cost and data requirements of full fine-tuning. Endotuning offers a more flexible and efficient way to adapt LLMs for various tasks and applications.