Show HN: Do Models Represent Epistemic Stance? (github.com)

A recent investigation has uncovered that language models can differentiate between the content of a statement and the epistemic stance under which it is framed, challenging our understanding of how these models reason with premises. The study tests how internal activations change when the same proposition is presented as either true or false, revealing that models are not merely responding to surface-level content but are capable of tracking how premises are licensed for reasoning. This was achieved using a controlled dataset, enabling the examination of model behavior under various epistemic conditions and leading to insights about the internal representation of epistemic stance. The significance of this research lies in its demonstration that epistemic stance is represented as a distinct, low-dimensional control signal within language models, fundamentally influencing downstream inference processes. Findings indicate that a model's ability to correctly adapt its reasoning based on premise framing is not dependent on global settings but occurs through localized adjustments in specific layers. The results open new avenues for understanding the internal mechanics of language models, particularly in how they encode and utilize assumption-based reasoning, which is critical for advancing AI systems toward more robust and nuanced comprehension of human language.