171 citations · 248 across the 20 of their papers we have counts for
6 papers · 1 filter
GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models
Tuo Wang, Adithya Kulkarni, Tyler Cody +3
Uncertainty estimation is essential for enhancing the reliability of Large Language Models (LLMs), particularly in high-stakes applications. Existing methods often overlook semanti…
Non-exchangeable Conformal Prediction for Temporal Graph Neural Networks
Tuo Wang, Jian Kang, Yujun Yan +2
Conformal prediction for graph neural networks (GNNs) offers a promising framework for quantifying uncertainty, enhancing GNN reliability in high-stakes applications. However, exis…
Spectral Insights into Data-Oblivious Critical Layers in Large Language Models
Xuyuan Liu, Lei Hsiung, Yaoqing Yang +1
Understanding how feature representations evolve across layers in large language models (LLMs) is key to improving their interpretability and robustness. While recent studies have…
Judging with Many Minds: Do More Perspectives Mean Less Prejudice? On Bias Amplifications and Resistance in Multi-Agent Based LLM-as-Judge
Chiyu Ma, Enpei Zhang, Yilun Zhao +7
LLM-as-Judge has emerged as a scalable alternative to human evaluation, enabling large language models (LLMs) to provide reward signals in trainings. While recent work has explored…
Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift
Yi Zhang, Elynn Chen, Yujun Yan
We study contextual dynamic pricing when a target market can leverage K auxiliary markets -- offline logs or concurrent streams -- whose mean utilities differ by a structured prefe…
MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-Text Decoding
Weikang Qiu, Zheng Huang, Haoyu Hu +3
Decoding functional magnetic resonance imaging (fMRI) signals into text has been a key challenge in the neuroscience community, with the potential to advance brain-computer interfa…