Imagen de Portada

Can LLMs Assist Expert Elicitation for Probabilistic Causal Modeling?

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Shaposhnyk, Olha, Zahorska, Daria, Yanushkevich, Svetlana
Formato:	Preprint
Publicado:	2025
Materias:	Artificial Intelligence Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Probabilistic causal graphs as categorical data synthesizers: Do they do better than Gaussian Copulas and Conditional Tabular GANs?
por: Shaposhnyk, Olha, et al.
Publicado: (2025)

Causal Preference Elicitation
por: Bonilla, Edwin V., et al.
Publicado: (2026)

CARE: Turning LLMs Into Causal Reasoning Expert
por: Dong, Juncheng, et al.
Publicado: (2025)

Counterfactual Probabilistic Diffusion with Expert Models
por: Mu, Wenhao, et al.
Publicado: (2025)

Causality Elicitation from Large Language Models
por: Kameyama, Takashi, et al.
Publicado: (2026)

Can LLMs Leverage Observational Data? Towards Data-Driven Causal Discovery with LLMs
por: Susanti, Yuni, et al.
Publicado: (2025)

Eliciting Numerical Predictive Distributions of LLMs Without Autoregression
por: Piskorz, Julianna, et al.
Publicado: (2026)

Can LLMs Score Medical Diagnoses and Clinical Reasoning as well as Expert Panels?
por: Rouillard, Amy, et al.
Publicado: (2026)

Causal Neural Probabilistic Circuits
por: Chen, Weixin, et al.
Publicado: (2026)

Dynamic Expert-Guided Model Averaging for Causal Discovery
por: Tench, Adrick, et al.
Publicado: (2026)

On the Relationship between Bayesian Networks and Probabilistic Structural Causal Models
por: Lucas, Peter J. F., et al.
Publicado: (2026)

The Triangle of Similarity: A Multi-Faceted Framework for Comparing Neural Network Representations
por: Sirikova, Olha, et al.
Publicado: (2026)

Can Post-Training Transform LLMs into Causal Reasoners?
por: Chen, Junqi, et al.
Publicado: (2026)

A Simple "Try Again" Can Elicit Multi-Turn LLM Reasoning
por: Liu, Licheng, et al.
Publicado: (2025)

The Elicitation Game: Evaluating Capability Elicitation Techniques
por: Hofstätter, Felix, et al.
Publicado: (2025)

Causal Imitation Learning under Expert-Observable and Expert-Unobservable Confounding
por: Shao, Daqian, et al.
Publicado: (2025)

Transcendence: Generative Models Can Outperform The Experts That Train Them
por: Zhang, Edwin, et al.
Publicado: (2024)

From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples?
por: Hiremath, Sujai, et al.
Publicado: (2025)

Learning to Defer for Causal Discovery with Imperfect Experts
por: Clivio, Oscar, et al.
Publicado: (2025)

ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts
por: Zhao, Heng, et al.
Publicado: (2026)

Let Experts Feel Uncertainty: A Multi-Expert Label Distribution Approach to Probabilistic Time Series Forecasting
por: Zhou, Zhen, et al.
Publicado: (2026)

Language Agents Meet Causality -- Bridging LLMs and Causal World Models
por: Gkountouras, John, et al.
Publicado: (2024)

Censored LLMs as a Natural Testbed for Secret Knowledge Elicitation
por: Casademunt, Helena, et al.
Publicado: (2026)

Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
por: Komanduri, Aneesh, et al.
Publicado: (2024)

Lookahead Unmasking Elicits Accurate Decoding in Diffusion Language Models
por: Lee, Sanghyun, et al.
Publicado: (2025)

SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions
por: Suvarna, Ashima, et al.
Publicado: (2026)

Exploiting the Experts: Unauthorized Compression in MoE-LLMs
por: Neogi, Pinaki Prasad Guha, et al.
Publicado: (2025)

Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?
por: Zeinaty, Christophe El, et al.
Publicado: (2025)

Are LLMs Good Cryptic Crossword Solvers?
por: Sadallah, Abdelrahman, et al.
Publicado: (2024)

LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
por: Kambhampati, Subbarao, et al.
Publicado: (2024)

Synthetic Error Injection Fails to Elicit Self-Correction In Language Models
por: Wu, David X., et al.
Publicado: (2025)

Self-Exploring Language Models: Active Preference Elicitation for Online Alignment
por: Zhang, Shenao, et al.
Publicado: (2024)

Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models
por: Wen, Yeming, et al.
Publicado: (2024)

Can Large Language Models Help Experimental Design for Causal Discovery?
por: Li, Junyi, et al.
Publicado: (2025)

Preference Elicitation for Offline Reinforcement Learning
por: Pace, Alizée, et al.
Publicado: (2024)

Personalized Algorithmic Recourse with Preference Elicitation
por: De Toni, Giovanni, et al.
Publicado: (2022)

RLAE: Reinforcement Learning-Assisted Ensemble for LLMs
por: Fu, Yuqian, et al.
Publicado: (2025)

Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms
por: Bystrova, Daria, et al.
Publicado: (2023)

RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs
por: Xu, Zhiyuan, et al.
Publicado: (2026)

OrdMoE: Preference Alignment via Hierarchical Expert Group Ranking in Multimodal Mixture-of-Experts LLMs
por: Gao, Yuting, et al.
Publicado: (2025)