Cover Image

Chance-Constrained Inference for Hallucination Risk Control in Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Mohandas, Sreenivasan
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On Learning for Ambiguous Chance Constrained Problems
by: Madhusudanarao, A Ch, et al.
Published: (2023)

Conformal Predictive Programming for Chance Constrained Optimization
by: Zhao, Yiqi, et al.
Published: (2024)

STEER: Inference-Time Risk Control via Constrained Quality-Diversity Search
by: Yang, Eric, et al.
Published: (2026)

A Gradient Guided Diffusion Framework for Chance Constrained Programming
by: Zhang, Boyang, et al.
Published: (2025)

Flipping-based Policy for Chance-Constrained Markov Decision Processes
by: Shen, Xun, et al.
Published: (2024)

Conformal Tail Risk Control for Large Language Model Alignment
by: Chen, Catherine Yu-Chi, et al.
Published: (2025)

Memory- and Latency-Constrained Inference of Large Language Models via Adaptive Split Computing
by: Sung, Mingyu, et al.
Published: (2025)

On the Fundamental Impossibility of Hallucination Control in Large Language Models
by: Karpowicz, Michał P.
Published: (2025)

On Large Language Models' Hallucination with Regard to Known Facts
by: Jiang, Che, et al.
Published: (2024)

Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control
by: Kato, Teruki, et al.
Published: (2026)

Large Language Models for Missing Data Imputation: Understanding Behavior, Hallucination Effects, and Control Mechanisms
by: Mangussi, Arthur Dantas, et al.
Published: (2026)

Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization
by: Tang, Zilu, et al.
Published: (2025)

Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systems
by: D'Silva, Rihan Aaron, et al.
Published: (2026)

Decoupling Contrastive Decoding: Robust Hallucination Mitigation in Multimodal Large Language Models
by: Chen, Wei, et al.
Published: (2025)

Leveraging Graph Structures to Detect Hallucinations in Large Language Models
by: Nonkes, Noa, et al.
Published: (2024)

Hallucination is Inevitable: An Innate Limitation of Large Language Models
by: Xu, Ziwei, et al.
Published: (2024)

Detecting and Preventing Hallucinations in Large Vision Language Models
by: Gunjal, Anisha, et al.
Published: (2023)

CausalGaze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models
by: Kong, Linggang, et al.
Published: (2026)

COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy
by: Wang, Lu, et al.
Published: (2024)

HEAL: An Empirical Study on Hallucinations in Embodied Agents Driven by Large Language Models
by: Chakraborty, Trishna, et al.
Published: (2025)

Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models
by: Binkowski, Jakub, et al.
Published: (2026)

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators
by: Chitty-Venkata, Krishna Teja, et al.
Published: (2024)

Inference-Time Reward Hacking in Large Language Models
by: Khalaf, Hadi, et al.
Published: (2025)

Unfamiliar Finetuning Examples Control How Language Models Hallucinate
by: Kang, Katie, et al.
Published: (2024)

Optimizing Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms
by: Neumann, Aneta, et al.
Published: (2020)

Scalable Token-Level Hallucination Detection in Large Language Models
by: Min, Rui, et al.
Published: (2026)

(Im)possibility of Automated Hallucination Detection in Large Language Models
by: Karbasi, Amin, et al.
Published: (2025)

Mitigating Hallucinations in Large Language Models via Causal Reasoning
by: Li, Yuangang, et al.
Published: (2025)

Semantic Membership Inference Attack against Large Language Models
by: Mozaffari, Hamid, et al.
Published: (2024)

Toward Inference-optimal Mixture-of-Expert Large Language Models
by: Yun, Longfei, et al.
Published: (2024)

Visual Hallucinations of Multi-modal Large Language Models
by: Huang, Wen, et al.
Published: (2024)

A Survey on Hallucination in Large Vision-Language Models
by: Liu, Hanchao, et al.
Published: (2024)

Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War
by: Jia, Zeyang, et al.
Published: (2023)

Fingerprinting Inference Systems of Large Language Models
by: Wimbauer, Anna, et al.
Published: (2026)

Fast Inference for Augmented Large Language Models
by: Shahout, Rana, et al.
Published: (2024)

Model-Distributed Inference for Large Language Models at the Edge
by: Macario, Davide, et al.
Published: (2025)

Generalizing Large Language Model Usability Across Resource-Constrained
by: Tsai, Yun-Da
Published: (2025)

Semi-Supervised Risk Control via Prediction-Powered Inference
by: Einbinder, Bat-Sheva, et al.
Published: (2024)

Manifold-based Sampling for In-Context Hallucination Detection in Large Language Models
by: Vamshi, Bodla Krishna, et al.
Published: (2026)

Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models
by: Siddiqui, S M Tahmid, et al.
Published: (2026)