LLMs struggle with accurate risk communication, study finds

A new research paper published on arXiv explores the limitations of large language models (LLMs) in communicating probabilistic risk information. The study found that while LLMs are generally consistent in their verbal descriptions of AI-generated predictions, they often fail to accurately reflect the underlying numerical quantities, a phenomenon termed miscalibration. This miscalibration is particularly pronounced when communicating uncertainty, suggesting that current LLMs are not yet reliable for standalone risk communication tasks. AI

IMPACT Current LLMs are not yet reliable for communicating probabilistic risk information, particularly uncertainty, limiting their use in critical applications.

RANK_REASON Research paper published on arXiv detailing LLM limitations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Beta distribution
• Diego Cerda-Mardini Sr.
• Hugging Face
• Large Language Models

• paper
• safety

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