Assessing a Model’s Knowledge Remains Open

Develop reliable methodologies to assess what factual knowledge a large language model possesses and to evaluate the quality of these methods despite the absence of ground truth about the model’s internal knowledge.

Background

The authors propose a four-category taxonomy derived from sampling-based estimates of how frequently a model outputs correct answers under different prompting and decoding conditions. This approach aims to label facts as ClearlyKnown, MaybeKnown, WeaklyKnown, or Unknown with respect to the pre-trained model.

Despite this contribution, they emphasize that rigorously assessing what a model truly knows is challenging because there is no direct ground truth about internal knowledge, rendering the general problem open and motivating further methodological advances.