Imagen de Portada

Saying More Than They Know: A Framework for Quantifying Epistemic-Rhetorical Miscalibration in Large Language Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Bakhshi, Asim D.
Formato:	Preprint
Publicado:	2026
Materias:	Computation and Language Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

When Models Know More Than They Say: Probing Analogical Reasoning in LLMs
por: McGovern, Hope, et al.
Publicado: (2026)

Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Models
por: Chen, Hao, et al.
Publicado: (2026)

When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration
por: Shi, Quan, et al.
Publicado: (2025)

Large Language Models are Miscalibrated In-Context Learners
por: Li, Chengzu, et al.
Publicado: (2023)

LLMs Know More Than Words: A Genre Study with Syntax, Metaphor & Phonetics
por: Shi, Weiye, et al.
Publicado: (2025)

What Models Know, How Well They Know It: Knowledge-Weighted Fine-Tuning for Learning When to Say "I Don't Know"
por: Lee, Joosung, et al.
Publicado: (2026)

Hallucination, Monofacts, and Miscalibration: An Empirical Investigation
por: Miao, Miranda Muqing, et al.
Publicado: (2025)

Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG
por: Chen, Xinxi, et al.
Publicado: (2024)

Epistemic Integrity in Large Language Models
por: Ghafouri, Bijean, et al.
Publicado: (2024)

"Lost-in-the-Later": Framework for Quantifying Contextual Grounding in Large Language Models
por: Tao, Yufei, et al.
Publicado: (2025)

LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations
por: Orgad, Hadas, et al.
Publicado: (2024)

Are Large Language Models Truly Smarter Than Humans?
por: M, Eshwar Reddy, et al.
Publicado: (2026)

KnowRL: Teaching Language Models to Know What They Know
por: Kale, Sahil, et al.
Publicado: (2025)

Do Large Language Models Know What They Are Capable Of?
por: Barkan, Casey O., et al.
Publicado: (2025)

Representations of Fact, Fiction and Forecast in Large Language Models: Epistemics and Attitudes
por: Li, Meng, et al.
Publicado: (2025)

Do Large Language Models Know How Much They Know?
por: Prato, Gabriele, et al.
Publicado: (2025)

Large Language Models Often Know When They Are Being Evaluated
por: Needham, Joe, et al.
Publicado: (2025)

Large Language Models Must Be Taught to Know What They Don't Know
por: Kapoor, Sanyam, et al.
Publicado: (2024)

Using Perspectival Words Is Harder Than Vocabulary Words for Humans and Even More So for Multimodal Language Models
por: Dong, Dota Tianai, et al.
Publicado: (2025)

Beyond Questions: Evaluating What Large Language Models (Actually) Know
por: Giordano, Luca, et al.
Publicado: (2026)

Solving for X and Beyond: Can Large Language Models Solve Complex Math Problems with More-Than-Two Unknowns?
por: Kao, Kuei-Chun, et al.
Publicado: (2024)

KnowGPT: Knowledge Graph based Prompting for Large Language Models
por: Zhang, Qinggang, et al.
Publicado: (2023)

KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
por: Lyu, Yougang, et al.
Publicado: (2024)

What is a Number, That a Large Language Model May Know It?
por: Marjieh, Raja, et al.
Publicado: (2025)

Chip-Tuning: Classify Before Language Models Say
por: Zhu, Fangwei, et al.
Publicado: (2024)

Pramana: Fine-Tuning Large Language Models for Epistemic Reasoning through Navya-Nyaya
por: Sathish, Sharath
Publicado: (2026)

CoDAR: Continuous Diffusion Language Models are More Powerful Than You Think
por: Shen, Junzhe, et al.
Publicado: (2026)

The Detection-Extraction Gap: Models Know the Answer Before They Can Say It
por: Wang, Hanyang, et al.
Publicado: (2026)

More Than Meets the Eye? Uncovering the Reasoning-Planning Disconnect in Training Vision-Language Driving Models
por: Song, Xurui, et al.
Publicado: (2025)

Do Retrieval Augmented Language Models Know When They Don't Know?
por: Zhou, Youchao, et al.
Publicado: (2025)

LLMs Struggle with Abstract Meaning Comprehension More Than Expected
por: Alhazmi, Hamoud, et al.
Publicado: (2026)

$(RSA)^2$: A Rhetorical-Strategy-Aware Rational Speech Act Framework for Figurative Language Understanding
por: Piano, Cesare Spinoso-Di, et al.
Publicado: (2025)

EpiK-Eval: Evaluation for Language Models as Epistemic Models
por: Prato, Gabriele, et al.
Publicado: (2023)

What Large Language Models Know and What People Think They Know
por: Steyvers, Mark, et al.
Publicado: (2024)

Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities
por: Lehmann, Hans Hergen, et al.
Publicado: (2025)

Making Metadata More FAIR Using Large Language Models
por: Sundaram, Sowmya S., et al.
Publicado: (2023)

Spike No More: Stabilizing the Pre-training of Large Language Models
por: Takase, Sho, et al.
Publicado: (2023)

The Polite Liar: Epistemic Pathology in Language Models
por: DeVilling, Bentley
Publicado: (2025)

VibeCheck: Discover and Quantify Qualitative Differences in Large Language Models
por: Dunlap, Lisa, et al.
Publicado: (2024)

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models
por: Min, Zeping, et al.
Publicado: (2025)