4 papers
Why is prompting hard? Understanding prompts on binary sequence predictors
Li Kevin Wenliang, Anian Ruoss, Jordi Grau-Moya +2
Frontier models can be prompted or conditioned to do many tasks, but finding good prompts is not always easy, nor is understanding some performant prompts. We view prompting as fin…
Thinking Like a Clinician: A Cognitive AI Agent for Clinical Diagnosis via Panoramic Profiling and Adversarial Debate
Zhiqi Lv, Duofan Tu, Jun Li +4
The application of large language models (LLMs) in clinical decision support faces significant challenges of "tunnel vision" and diagnostic hallucinations present in their processi…
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model
Mark Rowland, Li Kevin Wenliang, Rémi Munos +3
We propose a new algorithm for model-based distributional reinforcement learning (RL), and prove that it is minimax-optimal for approximating return distributions with a generative…
Amortized Planning with Large-Scale Transformers: A Case Study on Chess
Anian Ruoss, Grégoire Delétang, Sourabh Medapati +7
This paper uses chess, a landmark planning problem in AI, to assess transformers' performance on a planning task where memorization is futile $\unicode{x2013}$ even at a large scal…