Still No Lie Detector for Language Models: Probing Empirical and
  Conceptual Roadblocks
B. A. Levinstein Daniel A. Herrmann
We consider the questions of whether or not large language models (LLMs) have
beliefs, and, if they do, how we might measure them. First, we evaluate two
existing approaches, one due to Azaria and Mitchell (2023) and the other to
Burns et al. (2022). We provide empirical results that show that these methods
fail to generalize in very basic ways. We then argue that, even if LLMs have
beliefs, these methods are unlikely to be successful for conceptual reasons.
Thus, there is still no lie-detector for LLMs. After describing our empirical
results we take a step back and consider whether or not we should expect LLMs
to have something like beliefs in the first place. We consider some recent
arguments aiming to show that LLMs cannot have beliefs. We show that these
arguments are misguided. We provide a more productive framing of questions
surrounding the status of beliefs in LLMs, and highlight the empirical nature
of the problem. We conclude by suggesting some concrete paths for future work.