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Found 8 papers, 5 papers with code
Can Large Language Models Learn Independent Causal Mechanisms?
We show that such causal constraints can improve out-of-distribution performance on abstract and causal reasoning tasks.
Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks
Better understanding the natural world is a crucial task with a wide range of applications.
Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
When learnersourcing multiple-choice questions, creating explanations for the solution of a question is a crucial step; it helps other students understand the solution and promotes a deeper understanding of related concepts.
Meerkat Behaviour Recognition Dataset
Recording animal behaviour is an important step in evaluating the well-being of animals and further understanding the natural world.
Large Language Models Are Not Strong Abstract Reasoners
We perform extensive evaluations of state-of-the-art LLMs, showing that they currently achieve very limited performance in contrast with other natural language tasks, even when applying techniques that have been shown to improve performance on other NLP tasks.
Disentanglement of Latent Representations via Causal Interventions
Following this assumption, we introduce a new method for disentanglement inspired by causal dynamics that combines causality theory with vector-quantized variational autoencoders.
A Survey of Methods, Challenges and Perspectives in Causality
Deep Learning models have shown success in a large variety of tasks by extracting correlation patterns from high-dimensional data but still struggle when generalizing out of their initial distribution.
Relating Blindsight and AI: A Review
If we assume that artificial networks have no form of visual experience, then deficits caused by blindsight give us insights into the processes occurring within visual experience that we can incorporate into artificial neural networks.
