Saved in:
| Main Authors: | Ganju, Atul, McVoy, Travis, Dughmi, Shaddin, Teng, Shang-Hua |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.11302 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Optimal Stopping vs Best-of-$N$ for Inference Time Optimization
by: Kalayci, Yusuf, et al.
Published: (2025)
by: Kalayci, Yusuf, et al.
Published: (2025)
On the Limits of Language Generation: Trade-Offs Between Hallucination and Mode Collapse
by: Kalavasis, Alkis, et al.
Published: (2024)
by: Kalavasis, Alkis, et al.
Published: (2024)
PAC Learning is just Bipartite Matching (Sort of)
by: Dughmi, Shaddin
Published: (2025)
by: Dughmi, Shaddin
Published: (2025)
Proper Learnability and the Role of Unlabeled Data
by: Asilis, Julian, et al.
Published: (2025)
by: Asilis, Julian, et al.
Published: (2025)
Regularization and Optimal Multiclass Learning
by: Asilis, Julian, et al.
Published: (2023)
by: Asilis, Julian, et al.
Published: (2023)
Adaptive Generate-Rank-Verify: Inference-Time Search with Costly Verification
by: Dughmi, Shaddin, et al.
Published: (2026)
by: Dughmi, Shaddin, et al.
Published: (2026)
Transductive Learning Is Compact
by: Asilis, Julian, et al.
Published: (2024)
by: Asilis, Julian, et al.
Published: (2024)
Relatively Smart: A New Approach for Instance-Optimal Learning
by: Dughmi, Shaddin, et al.
Published: (2026)
by: Dughmi, Shaddin, et al.
Published: (2026)
ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models
by: Wang, Siwei, et al.
Published: (2024)
by: Wang, Siwei, et al.
Published: (2024)
The Price of Format: Diversity Collapse in LLMs
by: Yun, Longfei, et al.
Published: (2025)
by: Yun, Longfei, et al.
Published: (2025)
Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation
by: Hua, Zhenglin, et al.
Published: (2025)
by: Hua, Zhenglin, et al.
Published: (2025)
Real-Time Detection of Hallucinated Entities in Long-Form Generation
by: Obeso, Oscar, et al.
Published: (2025)
by: Obeso, Oscar, et al.
Published: (2025)
Benefits and Pitfalls of Reinforcement Learning for Language Model Planning: A Theoretical Perspective
by: Wang, Siwei, et al.
Published: (2025)
by: Wang, Siwei, et al.
Published: (2025)
Local Regularizers Are Not Transductive Learners
by: Jafar, Sky, et al.
Published: (2025)
by: Jafar, Sky, et al.
Published: (2025)
From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization
by: Zhou, Chenxi, et al.
Published: (2026)
by: Zhou, Chenxi, et al.
Published: (2026)
Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification
by: Kang, Haoqiang, et al.
Published: (2023)
by: Kang, Haoqiang, et al.
Published: (2023)
Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization
by: Tang, Zilu, et al.
Published: (2025)
by: Tang, Zilu, et al.
Published: (2025)
Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation
by: Zhang, Hongxiang, et al.
Published: (2025)
by: Zhang, Hongxiang, et al.
Published: (2025)
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
by: Chen, Shiqi, et al.
Published: (2024)
by: Chen, Shiqi, et al.
Published: (2024)
Detecting Mode Collapse in Language Models via Narration
by: Hamilton, Sil
Published: (2024)
by: Hamilton, Sil
Published: (2024)
Is Transductive Learning Equivalent to PAC Learning?
by: Dughmi, Shaddin, et al.
Published: (2024)
by: Dughmi, Shaddin, et al.
Published: (2024)
Neural Diversity Regularizes Hallucinations in Language Models
by: Chakrabarti, Kushal, et al.
Published: (2025)
by: Chakrabarti, Kushal, et al.
Published: (2025)
Generation Constraint Scaling Can Mitigate Hallucination
by: Kollias, Georgios, et al.
Published: (2024)
by: Kollias, Georgios, et al.
Published: (2024)
Large Language Models for Time Series: A Survey
by: Zhang, Xiyuan, et al.
Published: (2024)
by: Zhang, Xiyuan, et al.
Published: (2024)
SPA: A Simple but Tough-to-Beat Baseline for Knowledge Injection
by: Tang, Kexian, et al.
Published: (2026)
by: Tang, Kexian, et al.
Published: (2026)
On Characterizations for Language Generation: Interplay of Hallucinations, Breadth, and Stability
by: Kalavasis, Alkis, et al.
Published: (2024)
by: Kalavasis, Alkis, et al.
Published: (2024)
How to Synthesize Text Data without Model Collapse?
by: Zhu, Xuekai, et al.
Published: (2024)
by: Zhu, Xuekai, et al.
Published: (2024)
Hallucination is Inevitable: An Innate Limitation of Large Language Models
by: Xu, Ziwei, et al.
Published: (2024)
by: Xu, Ziwei, et al.
Published: (2024)
Markovian Generation Chains in Large Language Models
by: Geng, Mingmeng, et al.
Published: (2026)
by: Geng, Mingmeng, et al.
Published: (2026)
Detecting Hallucinations in SpeechLLMs at Inference Time Using Attention Maps
by: Waldendorf, Jonas, et al.
Published: (2026)
by: Waldendorf, Jonas, et al.
Published: (2026)
(Im)possibility of Automated Hallucination Detection in Large Language Models
by: Karbasi, Amin, et al.
Published: (2025)
by: Karbasi, Amin, et al.
Published: (2025)
Scalable Token-Level Hallucination Detection in Large Language Models
by: Min, Rui, et al.
Published: (2026)
by: Min, Rui, et al.
Published: (2026)
Unfamiliar Finetuning Examples Control How Language Models Hallucinate
by: Kang, Katie, et al.
Published: (2024)
by: Kang, Katie, et al.
Published: (2024)
Mitigating Hallucinations in Large Language Models via Causal Reasoning
by: Li, Yuangang, et al.
Published: (2025)
by: Li, Yuangang, et al.
Published: (2025)
Transcoders Trace Visual Grounding and Hallucinations in Vision-Language Models
by: Damianos, Dimitrios, et al.
Published: (2026)
by: Damianos, Dimitrios, et al.
Published: (2026)
Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability
by: Hron, Jiri, et al.
Published: (2024)
by: Hron, Jiri, et al.
Published: (2024)
Escaping Collapse: The Strength of Weak Data for Large Language Model Training
by: Amin, Kareem, et al.
Published: (2025)
by: Amin, Kareem, et al.
Published: (2025)
The Phenomenology of Hallucinations
by: Ruscio, Valeria, et al.
Published: (2026)
by: Ruscio, Valeria, et al.
Published: (2026)
FRED: Financial Retrieval-Enhanced Detection and Editing of Hallucinations in Language Models
by: Tan, Likun, et al.
Published: (2025)
by: Tan, Likun, et al.
Published: (2025)
CPR: Mitigating Large Language Model Hallucinations with Curative Prompt Refinement
by: Shim, Jung-Woo, et al.
Published: (2025)
by: Shim, Jung-Woo, et al.
Published: (2025)
Similar Items
-
Optimal Stopping vs Best-of-$N$ for Inference Time Optimization
by: Kalayci, Yusuf, et al.
Published: (2025) -
On the Limits of Language Generation: Trade-Offs Between Hallucination and Mode Collapse
by: Kalavasis, Alkis, et al.
Published: (2024) -
PAC Learning is just Bipartite Matching (Sort of)
by: Dughmi, Shaddin
Published: (2025) -
Proper Learnability and the Role of Unlabeled Data
by: Asilis, Julian, et al.
Published: (2025) -
Regularization and Optimal Multiclass Learning
by: Asilis, Julian, et al.
Published: (2023)