Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language
LLMs are increasingly deployed as post-hoc explainers of AI-generated outputs, yet it remains unclear whether they can reliably communicate probabilistic information in natural language. For this role to be viable, models must produce identical verbal descriptions for identical inputs, and select descriptions that accurately reflect the magnitude of the underlying numerical quantities. We evaluate whether nine LLMs meet these requirements within a two-stage prediction pipeline, in which an upstream model has produced probabilistic outputs characterized by their likelihood and uncertainty, and LLMs are tasked with selecting an appropriate verbal descriptor for each. We simulate predictions...
| Authors | Diego Cerda-Mardini, Sarath Chandar, Sreenath Madathil |
|---|---|
| Primary category | cs.CL |
| Published | 4 July 2026 |
| Last updated at source | 10 July 2026 |
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