7 papers
Conformalized Large Language Models under Configuration Shift
Yuqicheng Zhu, Jialin Yu, Lin Li +7
Conformal prediction (CP) is a distribution-free framework for uncertainty quantification that has recently been adapted to large language models (LLMs), providing prediction sets…
Foundation Models for Trajectory Planning in Autonomous Driving: A Review of Progress and Open Challenges
Kemal Oksuz, Alexandru Buburuzan, Anthony Knittel +2
The emergence of multi-modal foundation models has markedly transformed the technology for autonomous driving, shifting away from conventional and mostly hand-crafted design choice…
Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations
Kalyan Ramakrishnan, Jonathan G. Hedley, Sisi Qu +5
We train a neural network to predict distributional responses in gene expression following genetic perturbations. This is an essential task in early-stage drug discovery, where suc…
MObI: Multimodal Object Inpainting Using Diffusion Models
Alexandru Buburuzan, Anuj Sharma, John Redford +2
Safety-critical applications, such as autonomous driving, require extensive multimodal data for rigorous testing. Methods based on synthetic data are gaining prominence due to the…
Mixture of Experts Made Intrinsically Interpretable
Xingyi Yang, Constantin Venhoff, Ashkan Khakzar +4
Neurons in large language models often exhibit \emph{polysemanticity}, simultaneously encoding multiple unrelated concepts and obscuring interpretability. Instead of relying on pos…
AnnoCaseLaw: A Richly-Annotated Dataset For Benchmarking Explainable Legal Judgment Prediction
Magnus Sesodia, Alina Petrova, John Armour +5
Legal systems worldwide continue to struggle with overwhelming caseloads, limited judicial resources, and growing complexities in legal proceedings. Artificial intelligence (AI) of…