5 citations · 5 across the 3 of their papers we have counts for
5 papers
Dissecting Embodied Abilities in Multimodal Language Models through Skill-level Evaluation and Diagnosis
Yu Qi, Haibo Zhao, Ziyu Guo +17
Understanding the capability bottlenecks of embodied multimodal large language models (MLLMs) is crucial for improving embodied agents. However, existing embodied benchmarks mainly…
Fast Inference for Augmented Large Language Models
Rana Shahout, Cong Liang, Shiji Xin +4
Augmented Large Language Models (LLMs) enhance the capabilities of standalone LLMs by integrating external data sources through API calls. In interactive LLM applications, efficien…
GlobalTomo: A global dataset for physics-ML seismic wavefield modeling and FWI
Shiqian Li, Zhi Li, Zhancun Mu +6
Global seismic tomography, taking advantage of seismic waves from natural earthquakes, provides essential insights into the earth's internal dynamics. Advanced Full-waveform Invers…
MEWL: Few-shot multimodal word learning with referential uncertainty
Guangyuan Jiang, Manjie Xu, Shiji Xin +4
Without explicit feedback, humans can rapidly learn the meaning of words. Children can acquire a new word after just a few passive exposures, a process known as fast mapping. This…
On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization
Shiji Xin, Yifei Wang, Jingtong Su +1
Despite impressive success in many tasks, deep learning models are shown to rely on spurious features, which will catastrophically fail when generalized to out-of-distribution (OOD…