Stability of value-based preferences under systematic perturbations

Ascertain how value-based preferences in large language models behave under systematic perturbations, including whether such preferences remain stable across population-level variations induced by model dropout.

Background

For human-adjacent applications such as human-robot interaction, reliable and stable preferences are essential. The authors note uncertainty about whether any learned value-based preferences would persist under systematic perturbations (e.g., Monte Carlo dropout) applied to create model populations, which they use to assess brittleness.