7 papers · 1 filter
Adversarially Robust Abductive Fusion of Pre-trained Transformer-based Perception Models
Mario Leiva, Yue Ma, Qinru Qiu +2
Deploying pre-trained perception models in novel environments degrades their accuracy under distributional shift, and assembling them alone does not recover it: combiners such as m…
Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments
Mario Leiva, Noel Ngu, Joshua Shay Kricheli +6
The deployment of pre-trained perception models in novel environments often leads to performance degradation due to distributional shifts. Although recent artificial intelligence a…
Do Large Language Models Show Biases in Causal Learning? Insights from Contingency Judgment
MarÃa Victoria Carro, Denise Alejandra Mester, Francisca Gauna Selasco +4
Causal learning is the cognitive process of developing the capability of making causal inferences based on available information, often guided by normative principles. This process…
Error Detection and Correction for Interpretable Mathematics in Large Language Models
Yijin Yang, Cristina Cornelio, Mario Leiva +1
Recent large language models (LLMs) have demonstrated the ability to perform explicit multi-step reasoning such as chain-of-thought prompting. However, their intermediate steps oft…
A Conceptual Framework for AI Capability Evaluations
MarÃa Victoria Carro, Denise Alejandra Mester, Francisca Gauna Selasco +7
As AI systems advance and integrate into society, well-designed and transparent evaluations are becoming essential tools in AI governance, informing decisions by providing evidence…
Do Large Language Models Show Biases in Causal Learning?
Maria Victoria Carro, Francisca Gauna Selasco, Denise Alejandra Mester +4
Causal learning is the cognitive process of developing the capability of making causal inferences based on available information, often guided by normative principles. This process…