:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Shaposhnyk, Olha, Zahorska, Daria, Yanushkevich, Svetlana
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence Machine Learning
Online Access:	https://arxiv.org/abs/2504.10397
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Causal Preference Elicitation
by: Bonilla, Edwin V., et al.
Published: (2026)

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

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

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

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

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

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

Causal Neural Probabilistic Circuits
by: Chen, Weixin, et al.
Published: (2026)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

MATEO: A Multimodal Benchmark for Temporal Reasoning and Planning in LVLMs
by: Roccabruna, Gabriel, et al.
Published: (2026)

ElicitationGPT: Text Elicitation Mechanisms via Language Models
by: Wu, Yifan, et al.
Published: (2024)

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

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

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

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

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

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

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

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

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

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

BC Protocol: Structured Dual-Expert Dialogue for Eliciting High-Quality Chain-of-Thought Post-Training Data
by: Zou, Bo, et al.
Published: (2026)