Knowledge Elicitation

Knowledge elicitation aims to extract implicit knowledge embedded within complex systems, such as large language models or human experts, to improve their performance and understanding. Current research focuses on developing unsupervised methods to identify genuine knowledge within model activations, addressing challenges like distinguishing true knowledge from spurious correlations or simulated data, often employing techniques like cluster normalization and contrast-consistent search. This field is crucial for advancing AI capabilities in diverse applications, from improving chemical reaction prediction accuracy to enhancing human-AI collaboration in complex decision-making environments like military command and control.