Exploring the Limits of Fine-grained LLM-based Physics Inference via Premise Removal Interventions
This work addresses the problem of hallucination in language models for complex mathematical reasoning in physics, providing insights for researchers in AI and physics, though it is incremental as it builds on existing methods for assessing model capabilities.
The study investigated whether language models can perform physics-informed mathematical reasoning by systematically removing crucial context from prompts, finding that models ignore physical context and reverse-engineer solutions, leading to non-linear degradation in derivation quality with increased perturbation.
Language models (LMs) can hallucinate when performing complex mathematical reasoning. Physics provides a rich domain for assessing their mathematical capabilities, where physical context requires that any symbolic manipulation satisfies complex semantics (\textit{e.g.,} units, tensorial order). In this work, we systematically remove crucial context from prompts to force instances where model inference may be algebraically coherent, yet unphysical. We assess LM capabilities in this domain using a curated dataset encompassing multiple notations and Physics subdomains. Further, we improve zero-shot scores using synthetic in-context examples, and demonstrate non-linear degradation of derivation quality with perturbation strength via the progressive omission of supporting premises. We find that the models' mathematical reasoning is not physics-informed in this setting, where physical context is predominantly ignored in favour of reverse-engineering solutions.