3 citations · 14 across the 13 of their papers we have counts for
13 papers
Action100M: A Large-scale Video Action Dataset
Delong Chen, Tejaswi Kasarla, Yejin Bang +6
Inferring physical actions from visual observations is a fundamental capability for advancing machine intelligence in the physical world. Achieving this requires large-scale, open-…
VL-JEPA: Joint Embedding Predictive Architecture for Vision-language
Delong Chen, Mustafa Shukor, Theo Moutakanni +7
We introduce VL-JEPA, a vision-language model built on a Joint Embedding Predictive Architecture (JEPA). Instead of autoregressively generating tokens as in classical VLMs, VL-JEPA…
Planning with Reasoning using Vision Language World Model
Delong Chen, Theo Moutakanni, Willy Chung +4
Effective planning requires strong world models, but high-level world models that can understand and reason about actions with semantic and temporal abstraction remain largely unde…
WorldPrediction: A Benchmark for High-level World Modeling and Long-horizon Procedural Planning
Delong Chen, Willy Chung, Yejin Bang +2
Humans are known to have an internal "world model" that enables us to carry out action planning based on world states. AI agents need to have such a world model for action planning…
HalluLens: LLM Hallucination Benchmark
Yejin Bang, Ziwei Ji, Alan Schelten +5
Large language models (LLMs) often generate responses that deviate from user input or training data, a phenomenon known as "hallucination." These hallucinations undermine user trus…
Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations
Ziwei Ji, Lei Yu, Yeskendir Koishekenov +6
LLMs often adopt an assertive language style also when making false claims. Such ``overconfident hallucinations'' mislead users and erode trust. Achieving the ability to express in…