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…
High-Dimensional Interlingual Representations of Large Language Models
Bryan Wilie, Samuel Cahyawijaya, Junxian He +1
Large language models (LLMs) trained on massive multilingual datasets hint at the formation of interlingual constructs--a shared subspace in the representation space. However, evid…
LLM Internal States Reveal Hallucination Risk Faced With a Query
Ziwei Ji, Delong Chen, Etsuko Ishii +4
The hallucination problem of Large Language Models (LLMs) significantly limits their reliability and trustworthiness. Humans have a self-awareness process that allows us to recogni…
Belief Revision: The Adaptability of Large Language Models Reasoning
Bryan Wilie, Samuel Cahyawijaya, Etsuko Ishii +2
The capability to reason from text is crucial for real-world NLP applications. Real-world scenarios often involve incomplete or evolving data. In response, individuals update their…
What Makes for Good Image Captions?
Delong Chen, Samuel Cahyawijaya, Etsuko Ishii +3
This paper establishes a formal information-theoretic framework for image captioning, conceptualizing captions as compressed linguistic representations that selectively encode sema…
High-Dimension Human Value Representation in Large Language Models
Samuel Cahyawijaya, Delong Chen, Yejin Bang +5
The widespread application of LLMs across various tasks and fields has necessitated the alignment of these models with human values and preferences. Given various approaches of hum…
Measuring Political Bias in Large Language Models: What Is Said and How It Is Said
Yejin Bang, Delong Chen, Nayeon Lee +1
We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues. Existing benchmarks and measures focus o…
Mitigating Framing Bias with Polarity Minimization Loss
Yejin Bang, Nayeon Lee, Pascale Fung
Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events. Media outlets with divergent political stances often use…