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Found 4 papers, 2 papers with code
MedFuzz: Exploring the Robustness of Large Language Models in Medical Question Answering
Medical question-answering benchmarks rely on assumptions consistent with quantifying LLM performance but that may not hold in the open world of the clinic.
A Causal Bayesian Network and Probabilistic Programming Based Reasoning Framework for Robot Manipulation Under Uncertainty
Robot object manipulation in real-world environments is challenging because robot operation must be robust to a range of sensing, estimation, and actuation uncertainties to avoid potentially unsafe and costly mistakes that are a barrier to their adoption.
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
We find that prompting innovation can unlock deeper specialist capabilities and show that GPT-4 easily tops prior leading results for medical benchmarks.
Ranked #2 on
Question Answering
on MedQA
(using extra training data)
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems
In contrast, structural causal models support counterfactual inference, but do not identify the mechanisms.
