1 citations · 2 across the 4 of their papers we have counts for
5 papers
: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
Physical Intelligence, Bo Ai, Ali Amin +85
We present a new robotic foundation model, called , that can enable strong out-of-the-box performance in a wide range of scenarios. can follow diverse language i…
Deconstructing In-Context Learning: Understanding Prompts via Corruption
Namrata Shivagunde, Vladislav Lialin, Sherin Muckatira +1
The ability of large language models (LLMs) to learn in context based on the provided prompt has led to an explosive growth in their use, culminating in the proliferation of…
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang +40
The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between…
Emergent Abilities in Reduced-Scale Generative Language Models
Sherin Muckatira, Vijeta Deshpande, Vladislav Lialin +1
Large language models can solve new tasks without task-specific fine-tuning. This ability, also known as in-context learning (ICL), is considered an emergent ability and is primari…
Let's Reinforce Step by Step
Sarah Pan, Vladislav Lialin, Sherin Muckatira +1
While recent advances have boosted LM proficiency in linguistic benchmarks, LMs consistently struggle to reason correctly on complex tasks like mathematics. We turn to Reinforcemen…