5 papers
Calibrating LLMs with Semantic-level Reward
Fengfei Yu, Ruijia Niu, Dongxia Wu +2
As large language models (LLMs) are deployed in consequential settings such as medical question answering and legal reasoning, the ability to estimate when their outputs are likely…
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs
Ruijia Niu, Dongxia Wu, Rose Yu +1
Accurate uncertainty quantification in large language models (LLMs) is essential for reliable confidence estimation, yet fine-tuned LLMs often become overconfident under limited ad…
Zephyrus: An Agentic Framework for Weather Science
Sumanth Varambally, Marshall Fisher, Jas Thakker +14
Foundation models for weather science are pre-trained on vast amounts of structured numerical data and outperform traditional weather forecasting systems. However, these models lac…
SimulRAG: Simulator-based RAG for Grounding LLMs in Long-form Scientific QA
Haozhou Xu, Dongxia Wu, Matteo Chinazzi +3
Large Language Models (LLMs) show promise in generating long-form scientific explanations that synthesize evidence and connect multiple factors. However, in long-form scientific qu…
Diffusion-BBO: Diffusion-Based Inverse Modeling for Online Black-Box Optimization
Dongxia Wu, Nikki Lijing Kuang, Ruijia Niu +2
Online black-box optimization (BBO) aims to optimize an objective function by iteratively querying a black-box oracle in a sample-efficient way. While prior studies focus on forwar…