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Found 7 papers, 3 papers with code
Comparing Machines and Children: Using Developmental Psychology Experiments to Assess the Strengths and Weaknesses of LaMDA Responses
We propose that using classical experiments from child development is a particularly effective way to probe the computational abilities of AI models, in general, and LLMs in particular.
Imitation versus Innovation: What children can do that large language and language-and-vision models cannot (yet)?
Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents.
Towards Understanding How Machines Can Learn Causal Overhypotheses
Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence.
Learning Causal Overhypotheses through Exploration in Children and Computational Models
Despite recent progress in reinforcement learning (RL), RL algorithms for exploration still remain an active area of research.
Exploring Exploration: Comparing Children with RL Agents in Unified Environments
Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn.
Exploiting Attention to Reveal Shortcomings in Memory Models
The decision making processes of deep networks are difficult to understand and while their accuracy often improves with increased architectural complexity, so too does their opacity.
Evaluating Theory of Mind in Question Answering
We propose a new dataset for evaluating question answering models with respect to their capacity to reason about beliefs.
