4 papers
Do VLMs Align Better with Humans than LLMs during Natural Reading?
Jinzhou Wu, Zhengwu Ma, Jixing Li +2
Large language models have become increasingly useful computational models of human language processing, but it remains open whether vision-language learning makes text representat…
When Brain Foundation Model Meets Cauchy-Schwarz Divergence: A New Framework for Cross-Subject Motor Imagery Decoding
Jinzhou Wu, Baoping Tang, Qikang Li +3
Decoding motor imagery (MI) electroencephalogram (EEG) signals, a key non-invasive brain-computer interface (BCI) paradigm for controlling external systems, has been significantly…
Democratic Preference Alignment via Sortition-Weighted RLHF
Suvadip Sana, Jinzhou Wu, Martin T. Wells
Whose values should AI systems learn? Preference based alignment methods like RLHF derive their training signal from human raters, yet these rater pools are typically convenience s…
AI Deception: Risks, Dynamics, and Controls
Boyuan Chen, Sitong Fang, Jiaming Ji +56
As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an…