Calibrated Language Models Must Hallucinate
Fuente:
arXiv
Saved in:
| Main Authors: | Kalai, Adam Tauman, Vempala, Santosh S. |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Why Language Models Hallucinate
by: Kalai, Adam Tauman, et al.
Published: (2025)
by: Kalai, Adam Tauman, et al.
Published: (2025)
Do Language Models Know When They're Hallucinating References?
by: Agrawal, Ayush, et al.
Published: (2023)
by: Agrawal, Ayush, et al.
Published: (2023)
Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
by: Suzgun, Mirac, et al.
Published: (2024)
by: Suzgun, Mirac, et al.
Published: (2024)
Consensus Sampling for Safer Generative AI
by: Kalai, Adam Tauman, et al.
Published: (2025)
by: Kalai, Adam Tauman, et al.
Published: (2025)
Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation
by: Zelikman, Eric, et al.
Published: (2023)
by: Zelikman, Eric, et al.
Published: (2023)
First-Person Fairness in Chatbots
by: Eloundou, Tyna, et al.
Published: (2024)
by: Eloundou, Tyna, et al.
Published: (2024)
HAVE: Head-Adaptive Gating and ValuE Calibration for Hallucination Mitigation in Large Language Models
by: Tong, Xin, et al.
Published: (2025)
by: Tong, Xin, et al.
Published: (2025)
How Open Must Language Models be to Enable Reliable Scientific Inference?
by: Michaelov, James A., et al.
Published: (2026)
by: Michaelov, James A., et al.
Published: (2026)
Alleviating Hallucinations of Large Language Models through Induced Hallucinations
by: Zhang, Yue, et al.
Published: (2023)
by: Zhang, Yue, et al.
Published: (2023)
On Non-interactive Evaluation of Animal Communication Translators
by: Paradise, Orr, et al.
Published: (2025)
by: Paradise, Orr, et al.
Published: (2025)
Hallucination Detection with Small Language Models
by: Cheung, Ming
Published: (2025)
by: Cheung, Ming
Published: (2025)
Quantifying Hallucinations in Language Language Models on Medical Textbooks
by: Colelough, Brandon C., et al.
Published: (2026)
by: Colelough, Brandon C., et al.
Published: (2026)
Large Language Models Must Be Taught to Know What They Don't Know
by: Kapoor, Sanyam, et al.
Published: (2024)
by: Kapoor, Sanyam, et al.
Published: (2024)
An Evolutionary Large Language Model for Hallucination Mitigation
by: Boulesnane, Abdennour, et al.
Published: (2024)
by: Boulesnane, Abdennour, et al.
Published: (2024)
Triggering Hallucinations in LLMs: A Quantitative Study of Prompt-Induced Hallucination in Large Language Models
by: Sato, Makoto
Published: (2025)
by: Sato, Makoto
Published: (2025)
ANAH: Analytical Annotation of Hallucinations in Large Language Models
by: Ji, Ziwei, et al.
Published: (2024)
by: Ji, Ziwei, et al.
Published: (2024)
Confabulation: The Surprising Value of Large Language Model Hallucinations
by: Sui, Peiqi, et al.
Published: (2024)
by: Sui, Peiqi, et al.
Published: (2024)
Copy-Paste to Mitigate Large Language Model Hallucinations
by: Long, Yongchao, et al.
Published: (2025)
by: Long, Yongchao, et al.
Published: (2025)
The Impact of Negated Text on Hallucination with Large Language Models
by: Seo, Jaehyung, et al.
Published: (2025)
by: Seo, Jaehyung, et al.
Published: (2025)
Theoretical Foundations and Mitigation of Hallucination in Large Language Models
by: Gumaan, Esmail
Published: (2025)
by: Gumaan, Esmail
Published: (2025)
HICD: Hallucination-Inducing via Attention Dispersion for Contrastive Decoding to Mitigate Hallucinations in Large Language Models
by: Jiang, Xinyan, et al.
Published: (2025)
by: Jiang, Xinyan, et al.
Published: (2025)
How Large Language Models are Designed to Hallucinate
by: Ackermann, Richard, et al.
Published: (2025)
by: Ackermann, Richard, et al.
Published: (2025)
Credence Calibration Game? Calibrating Large Language Models through Structured Play
by: Fang, Ke, et al.
Published: (2025)
by: Fang, Ke, et al.
Published: (2025)
HALO: An Ontology for Representing and Categorizing Hallucinations in Large Language Models
by: Nananukul, Navapat, et al.
Published: (2023)
by: Nananukul, Navapat, et al.
Published: (2023)
Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization
by: Chrysostomou, George, et al.
Published: (2023)
by: Chrysostomou, George, et al.
Published: (2023)
Beyond Facts: Evaluating Intent Hallucination in Large Language Models
by: Hao, Yijie, et al.
Published: (2025)
by: Hao, Yijie, et al.
Published: (2025)
Mechanistic Understanding and Mitigation of Language Model Non-Factual Hallucinations
by: Yu, Lei, et al.
Published: (2024)
by: Yu, Lei, et al.
Published: (2024)
Reference-free Hallucination Detection for Large Vision-Language Models
by: Li, Qing, et al.
Published: (2024)
by: Li, Qing, et al.
Published: (2024)
Neural Probe-Based Hallucination Detection for Large Language Models
by: Liang, Shize, et al.
Published: (2025)
by: Liang, Shize, et al.
Published: (2025)
Counterfactual Probing for Hallucination Detection and Mitigation in Large Language Models
by: Feng, Yijun
Published: (2025)
by: Feng, Yijun
Published: (2025)
LettuceDetect: A Hallucination Detection Framework for RAG Applications
by: Kovács, Ádám, et al.
Published: (2025)
by: Kovács, Ádám, et al.
Published: (2025)
Calibrating Verbalized Probabilities for Large Language Models
by: Wang, Cheng, et al.
Published: (2024)
by: Wang, Cheng, et al.
Published: (2024)
Calibrating Reasoning in Language Models with Internal Consistency
by: Xie, Zhihui, et al.
Published: (2024)
by: Xie, Zhihui, et al.
Published: (2024)
HALT-RAG: A Task-Adaptable Framework for Hallucination Detection with Calibrated NLI Ensembles and Abstention
by: Goswami, Saumya, et al.
Published: (2025)
by: Goswami, Saumya, et al.
Published: (2025)
MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Models
by: Agarwal, Vibhor, et al.
Published: (2024)
by: Agarwal, Vibhor, et al.
Published: (2024)
Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models
by: Lv, Qitan, et al.
Published: (2024)
by: Lv, Qitan, et al.
Published: (2024)
HIDE and Seek: Detecting Hallucinations in Language Models via Decoupled Representations
by: Chatterjee, Anwoy, et al.
Published: (2025)
by: Chatterjee, Anwoy, et al.
Published: (2025)
Delta -- Contrastive Decoding Mitigates Text Hallucinations in Large Language Models
by: Huang, Cheng Peng, et al.
Published: (2025)
by: Huang, Cheng Peng, et al.
Published: (2025)
Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models
by: Chen, Yuyan, et al.
Published: (2024)
by: Chen, Yuyan, et al.
Published: (2024)
Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models
by: Sikka, Varin, et al.
Published: (2025)
by: Sikka, Varin, et al.
Published: (2025)
Similar Items
-
Why Language Models Hallucinate
by: Kalai, Adam Tauman, et al.
Published: (2025) -
Do Language Models Know When They're Hallucinating References?
by: Agrawal, Ayush, et al.
Published: (2023) -
Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
by: Suzgun, Mirac, et al.
Published: (2024) -
Consensus Sampling for Safer Generative AI
by: Kalai, Adam Tauman, et al.
Published: (2025) -
Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation
by: Zelikman, Eric, et al.
Published: (2023)