Human intelligence is compositional: there is no “general-purpose” human and we are all specialized in our own ways, while collaboration protocols guide diverse individuals to come together and achieve what they cannot on their own. Moving beyond single monolithic AI models, I aim to advance compositional intelligence through model collaboration, where multiple (language) models collaborate, compose, and complement each other. In this talk I discuss three specific protocols of model collaboration: Model Swarms, multiple LMs collaboratively search in the model weight space for adaptation; Sparta Alignment, multiple LMs collectively evolve via competition and combat; and Switch Generation, pretrained and aligned stages of LMs take turns to generate segments of responses to patch the tradeoffs of RL/alignment. These protocols span diverse levels of model access and information exchange, spearheading a new paradigm of AI systems featuring compositional intelligence and collaborative development.
Shangbin Feng is a PhD student at the University of Washington. He works on model collaboration, where multiple models collaborate and compose through diverse protocols to advance collaborative development and compositional intelligence. His research has won the best paper award at ACL 2023, the outstanding paper award at ACL 2024, the IBM PhD Fellowship, the Jane Street Graduate Research Fellowship, the Baidu PhD Fellowship, the NVIDIA Graduate Fellowship, and was covered by the Washington Post and MIT Technology Review.