Inference Time Intervention

Inference-time intervention (ITI) is a technique for improving the performance of large language models (LLMs) without retraining, focusing primarily on enhancing truthfulness and mitigating issues like copyright infringement. Current research explores various ITI methods, including manipulating attention mechanisms and employing sparsity-guided techniques to identify and adjust internal model representations during inference. This approach offers a data-efficient and computationally inexpensive way to address LLM limitations, potentially leading to more reliable and responsible deployment of these powerful models in various applications.