11 papers
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…
AI Debaters are More Persuasive when Arguing in Alignment with Their Own Beliefs
María Victoria Carro, Denise Alejandra Mester, Facundo Nieto +9
The core premise of AI debate as a scalable oversight technique is that it is harder to lie convincingly than to refute a lie, enabling the judge to identify the correct position.…
Do Large Language Models Show Biases in Causal Learning? Insights from Contingency Judgment
María 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
María 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…
Machine Learning Model Integration with Open World Temporal Logic for Process Automation
Dyuman Aditya, Colton Payne, Mario Leiva +1
Recent advances in Machine Learning (ML) have produced models that extract structured information from complex data. However, a significant challenge lies in translating these perc…
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…
VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection
Aditya Taparia, Noel Ngu, Mario Leiva +6
Although fusing multiple sensor modalities can enhance object detection performance, existing fusion approaches often overlook subtle variations in environmental conditions and sen…
Multiple Distribution Shift -- Aerial (MDS-A): A Dataset for Test-Time Error Detection and Model Adaptation
Noel Ngu, Aditya Taparia, Gerardo I. Simari +5
Machine learning models assume that training and test samples are drawn from the same distribution. As such, significant differences between training and test distributions often l…
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…
Are UFOs Driving Innovation? The Illusion of Causality in Large Language Models
María Victoria Carro, Francisca Gauna Selasco, Denise Alejandra Mester +1
Illusions of causality occur when people develop the belief that there is a causal connection between two variables with no supporting evidence. This cognitive bias has been propos…