Do Large Language Models Know What They Don't Know II? A Fully Behavioral, Non-Cognitive Measure of Epistemic Honesty

• URL: http://arxiv.org/abs/2609.07879v1
• Date: Mon, 07 Sep 2026 18:40:08 GMT
• Title: Do Large Language Models Know What They Don't Know II? A Fully Behavioral, Non-Cognitive Measure of Epistemic Honesty
• Abstract summary: Large Language Models (LLMs) are frequently confident, eloquent, and well versed.<n>Do they know what they don't know?<n>We develop a novel metric to evaluate whether an LLM appropriately acknowledges the boundaries of its knowledge.
• Score: 8.148357447770893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are frequently confident, eloquent, and well versed. A natural question arises: do they know what they don't know? To answer this question, we borrow the concept of epistemic honesty and develop a novel metric to systematically evaluate whether an LLM appropriately acknowledges the boundaries of its knowledge. In this work, we introduce the Epistemic Honesty Quotient (EHQ), which reports three observable sub-scores across two operational axes (epistemic restraint and substantive-answer calibration), and construct EHQ-3000, a 3,000-question benchmark spanning Fabricated Entity, Post-Cutoff Event, Hyper-Niche True, and Context-Conditioned Questions. From a frozen registry of 21 model API routes, 15 completed the protocol after endpoint and eligibility checks; 14 entered the confirmatory analysis because severe provider-side truncation made one route's score indeterminate. The study reveals substantial variation across models, including a difference that can not be explained by their capability to extract explicitly available information. Composite EHQ ranges from 0.31 to 0.81 across the analysed panel, despite near-ceiling performance on the document-grounded capability probe. The two restraint criteria overlap strongly under the present category composition, whereas substantive-answer calibration varies across models and does not reliably co-vary with restraint; however, the small panel leaves substantial uncertainty. Thus, EHQ reveals behavioral differences that are not visible to conventional correctness-based assessment, while also showing why dataset composition, provider behavior, and confidence elicitation must remain part of the interpretation.

Related papers

• Noēsis: Deterministic-First Retrieval with Two-Tier Context Hydration for Factuality-Critical Queries on Small Local Models [0.0]
  We present Noesis, the deterministic-first query plane of the Noesis architecture.<n>Noesis makes every deterministic judgment before generation.<n>Every reported value is traceable to its exact source and position by construction.
  arXiv  Detail & Related papers  (2026-09-07T15:49:16Z)
• The Imperfective Paradox Is Not Necessarily in Large Language Models: A Benchmark Failure Before a Model Failure [42.25959731143407]
  We examine the benchmark and conclusions and show that it is substantially affected by conceptual and evaluation mis-specifications.<n>Under a strict NLI standard, 76% of Group A instances do not explicitly rule out culmination.<n>We formulate event-semantic NLI as a Multi-step Reasoning Problem and assess both intermediate semantic decisions and final predictions.
  arXiv  Detail & Related papers  (2026-08-25T18:00:18Z)
• Comparing Large Language Models on Scrum Certification-Style Questions: Accuracy, Stability, and Error Patterns [0.19674381684480705]
  Large Language Models (LLMs) are increasingly used in exam- and certification-style question answering tasks.<n>This paper evaluates the performance of three contemporary LLMs, textitGPT-5 mini, textitGemini 3 Flash, and textitDeepSeek Chat 3.2, in answering 993 Scrum certification-style questions.
  arXiv  Detail & Related papers  (2026-06-29T23:37:56Z)
• LLMs Show No Signs Of Individuated Metacognition [0.023227405857540805]
  We decompose binary confidence judgements from 20 Large Language Models.<n>We ask whether two models that differ in confidence also differ in performance.<n>We find no evidence for significant verbalised individuated metacognition in any tested domain.
  arXiv  Detail & Related papers  (2026-05-22T23:54:33Z)
• Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models [54.041320081289996]
  We present the first systematic empirical study of uncertainty estimation in audio-aware large language models (ALLMs)<n>We benchmark five representative methods, including predictive entropy, length-normalized entropy, semantic entropy, discrete semantic entropy, and P(True)<n>Our results reveal two key findings. First, semantic-level and verification-based methods consistently outperform token-level baselines on general audio reasoning benchmarks.
  arXiv  Detail & Related papers  (2026-04-28T12:56:22Z)
• PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervised Finetuning [0.0]
  We study decision-aware query resolution under incomplete information, where a model must determine whether to Answer, Ask for clarification, or Abstain.<n>We propose PassiveQA, a three-action framework that aligns model behaviour with information sufficiency through supervised finetuning.<n>Our approach integrates structured information-state representations, knowledge graph-grounded context, and a finetuned planner that explicitly models missing variables and decision reasoning.
  arXiv  Detail & Related papers  (2026-04-06T09:54:16Z)
• AI Knows What's Wrong But Cannot Fix It: Helicoid Dynamics in Frontier LLMs Under High-Stakes Decisions [51.56484100374058]
  Helicoid dynamics is the name given to a specific failure regime in that second domain.<n>System engages competently, drifts into error, accurately names what went wrong, then reproduces the same pattern at a higher level of sophistication.<n>This prospective case series documents that regime across seven leading systems.
  arXiv  Detail & Related papers  (2026-03-12T05:25:49Z)
• Decomposed Prompting Does Not Fix Knowledge Gaps, But Helps Models Say "I Don't Know" [47.930782177987446]
  Large language models often struggle to recognize their knowledge limits in closed-book question answering, leading to confident hallucinations.<n>We evaluate three task-equivalent prompting regimes: Direct, Assistive, and Incremental, across different model scales and multi-hop QA benchmarks.<n>Because factual knowledge is stable while hallucinations are agreement, cross-regime provides a precise signal of internal uncertainty.
  arXiv  Detail & Related papers  (2026-02-04T18:39:58Z)
• Encyclo-K: Evaluating LLMs with Dynamically Composed Knowledge Statements [78.87065404966002]
  Existing benchmarks predominantly curate questions at the question level.<n>We propose Encyclo-K, a statement-based benchmark that rethinks benchmark construction from the ground up.
  arXiv  Detail & Related papers  (2025-12-31T13:55:54Z)
• CLEAR-3K: Assessing Causal Explanatory Capabilities in Language Models [3.137688620241855]
  We introduce CLEAR-3K, a dataset of 3,000 assertion-reasoning questions designed to evaluate whether language models can determine if one statement causally explains another.<n>Each question present an assertion-reason pair and challenge language models to distinguish between semantic relatedness and genuine causal explanatory relationships.
  arXiv  Detail & Related papers  (2025-06-20T17:35:36Z)
• Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness [106.52630978891054]
  We present a taxonomy of uncertainty specific to vision-language AI systems. We also introduce a new metric confidence-weighted accuracy, that is well correlated with both accuracy and calibration error.
  arXiv  Detail & Related papers  (2024-07-02T04:23:54Z)
• Improving the Reliability of Large Language Models by Leveraging Uncertainty-Aware In-Context Learning [76.98542249776257]
  Large-scale language models often face the challenge of "hallucination" We introduce an uncertainty-aware in-context learning framework to empower the model to enhance or reject its output in response to uncertainty.
  arXiv  Detail & Related papers  (2023-10-07T12:06:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.