View From Above: A Framework for Evaluating Distribution Shifts in Model Behavior
<|reference_start|>View From Above: A Framework for Evaluating Distribution Shifts in Model Behavior: When large language models (LLMs) are asked to perform certain tasks, how can we be sure that their learned representations align with reality? We propose a domain-agnostic framework for systematically evaluating distribution shifts in LLMs decision-making processes, where they are given control of mechanisms governed by pre-defined rules. While individual LLM actions may appear consistent with expected behavior, across a large number of trials, statistically significant distribution shifts can emerge. To test this, we construct a well-defined environment with known outcome logic: blackjack. In more than 1,000 trials, we uncover statistically significant evidence suggesting behavioral misalignment in the learned representations of LLM.<|reference_end|>
@article{chopra2024view,
    title={View From Above: A Framework for Evaluating Distribution Shifts in Model
  Behavior},
    author={Tanush Chopra, Michael Li, Jacob Haimes},
    journal={arXiv preprint arXiv:2407.00948},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00948},
    primaryClass={cs.CL cs.AI cs.LG}
}