Performance Analysis
Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making Manling Li
We aim to evaluate Large Language Models (LLMs) for embodied decision making.
DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios
Detecting text generated by large language models (LLMs) is of great recent interest.
OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning
Semi-supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data. Traditionally, SSL mandates that all classes possess labeled instances.
Membership Inference Attacks against Large Vision-Language Models
We believe that our work can deepen the understanding and methodology of MIAs in the context of VLLMs.
Scale-invariant Optimal Sampling for Rare-events Data with Sparse Models
Rare-events data refer to binary-response data that are highly imbalanced, i.e., the number of zeros
