  Large language models (LLMs) demonstrate outstanding capabilities, but
challenges remain regarding their ability to solve complex reasoning tasks, as
well as their transparency, robustness, truthfulness, and ethical alignment. In
this preliminary study, we compile a set of core principles for steering and
evaluating the reasoning of LLMs by curating literature from several relevant
strands of work: structured reasoning in LLMs, self-evaluation/self-reflection,
explainability, AI system safety/security, guidelines for human critical
thinking, and ethical/regulatory guidelines for AI. We identify and curate a
list of 220 principles from literature, and derive a set of 37 core principles
organized into seven categories: assumptions and perspectives, reasoning,
information and evidence, robustness and security, ethics, utility, and
implications. We conduct a small-scale expert survey, eliciting the subjective
importance experts assign to different principles and lay out avenues for
future work beyond our preliminary results. We envision that the development of
a shared model of principles can serve multiple purposes: monitoring and
steering models at inference time, improving model behavior during training,
and guiding human evaluation of model reasoning.
