A new research paper explores the discrepancies between how humans and large language models (LLMs) express linguistic uncertainty. While humans use verbal markers like "possible" or "likely" to reflect their knowledge boundaries, current LLM uncertainty quantification often relies on signal costing. The study introduces a novel algorithm called METHODNAME, which learns an optimal uncertainty profile directly from LLM outputs, enabling a direct comparison of confidence semantics between humans and LLMs and revealing systematic disparities in verbal expressions. AI
IMPACT This research highlights potential differences in how LLMs and humans interpret and express uncertainty, which could impact the reliability and interpretability of AI systems.
RANK_REASON The cluster contains a research paper published on arXiv detailing a novel algorithm and findings. [lever_c_demoted from research: ic=1 ai=1.0]
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