Can LLMs Use Linguistic Uncertainty Markers to Reliably Reflect Intrinsic Confidence?
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
Researchers have formalized a framework for measuring whether language models can reliably map their internal confidence levels onto linguistic uncertainty markers like 'likely' or 'probably'. The work introduces marker internal confidence (MIC) as a measurable construct and proposes seven stability metrics to test whether models apply these expressions consistently across tasks and distributions. This addresses a critical gap in LLM interpretability: even if models express doubt linguistically, those expressions may not track their actual uncertainty in predictable ways. The findings matter for deployment contexts where users rely on model hedging as a signal of reliability.
Modelwire’s AI-generated summary of coverage from arXiv cs.CL.
Modelwire analysis
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The paper's contribution is not just observing that models hedge inconsistently, but formalizing seven stability metrics that make inconsistency measurable and comparable across models. That operationalization is what moves this from a philosophical concern about honesty to something engineers can actually test against.
This connects directly to the colloquial Malay discourse particle work covered the same day ('Can Large Language Models Handle Discourse Particles?'), which benchmarked whether models process hedges and fillers correctly in a non-English context. That paper asked whether models understand uncertainty markers as linguistic input; this paper asks whether models produce them faithfully as output. Together they bracket the same problem from opposite ends: comprehension and generation of linguistic uncertainty are both unreliable in ways current evaluations miss. The abstraction gap work on VLMs ('The Abstraction Gap in Vision-Language Causal Reasoning') adds a third angle, showing that fluent output routinely masks shallow reasoning, which is precisely the failure mode MIC is designed to detect in text-only models.
Watch whether any of the major model evaluation suites (HELM, BIG-Bench successors) adopt MIC or the seven stability metrics within the next two release cycles. Adoption there would signal the field treating calibrated hedging as a first-class evaluation criterion rather than a research curiosity.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.CL
Can Large Language Models Handle Discourse Particles? A Case Study of Colloquial Malay
Researchers have built the first systematic benchmark for evaluating how well large language models handle discourse particles in colloquial Malay, filling a critical gap in LLM evaluation beyond English-centric benchmarks. Discourse particles like filler words and hedges are essential for natural human communication but remain understudied in non-English contexts. The MalayPrag benchmark introduces a linguistically…
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