most citedOnionEval: An Unified Evaluation of Fact-conflicting Hallucination for Small-Large Language Models

2 citations · 2 across the 6 of their papers we have counts for

collaborators

9 papers

eess.SY2025

Causal Feature Selection for Weather-Driven Residential Load Forecasting

Elise Zhang, François Mirallès, Stéphane Dellacherie +2

Weather is a dominant external driver of residential electricity demand, but adding many meteorological covariates can inflate model complexity and may even impair accuracy. Select…

cs.AI2025

STEMS: Spatial-Temporal Enhanced Safe Multi-Agent Coordination for Building Energy Management

Huiliang Zhang, Di Wu, Arnaud Zinflou +1

Building energy management is essential for achieving carbon reduction goals, improving occupant comfort, and reducing energy costs. Coordinated building energy management faces cr…

cs.LG2025

Leveraging Multivariate Long-Term History Representation for Time Series Forecasting

Huiliang Zhang, Di Wu, Arnaud Zinflou +3

Multivariate Time Series (MTS) forecasting has a wide range of applications in both industry and academia. Recent advances in Spatial-Temporal Graph Neural Network (STGNN) have ach…

cs.AI2025

Leveraging LLMs as Meta-Judges: A Multi-Agent Framework for Evaluating LLM Judgments

Yuran Li, Jama Hussein Mohamud, Chongren Sun +2

Large language models (LLMs) are being widely applied across various fields, but as tasks become more complex, evaluating their responses is increasingly challenging. Compared to h…

cs.LG2025

MXMap: A Multivariate Cross Mapping Framework for Causal Discovery in Dynamical Systems

Elise Zhang, François Mirallès, Raphaël Rousseau-Rizzi +3

Convergent Cross Mapping (CCM) is a powerful method for detecting causality in coupled nonlinear dynamical systems, providing a model-free approach to capture dynamic causal intera…

cs.CL20252 cited

OnionEval: An Unified Evaluation of Fact-conflicting Hallucination for Small-Large Language Models

Chongren Sun, Yuran Li, Di Wu +1

Large Language Models (LLMs) are highly capable but require significant computational resources for both training and inference. Within the LLM family, smaller models (those with f…