5 papers · 1 filter
Overcoming classic challenges for artificial neural networks by providing incentives and practice
Kazuki Irie, Brenden M. Lake
Since the earliest proposals for artificial neural network (ANN) models of the mind and brain, critics have pointed out key weaknesses in these models compared to human cognitive a…
Do Large Language Models Reason Causally Like Us? Even Better?
Hanna M. Dettki, Brenden M. Lake, Charley M. Wu +1
Causal reasoning is a core component of intelligence. Large language models (LLMs) have shown impressive capabilities in generating human-like text, raising questions about whether…
SAGE-Eval: Evaluating LLMs for Systematic Generalizations of Safety Facts
Chen Yueh-Han, Guy Davidson, Brenden M. Lake
Do LLMs robustly generalize critical safety facts to novel situations? Lacking this ability is dangerous when users ask naive questions. For instance, "I'm considering packing melo…
Goals as Reward-Producing Programs
Guy Davidson, Graham Todd, Julian Togelius +2
People are remarkably capable of generating their own goals, beginning with child's play and continuing into adulthood. Despite considerable empirical and computational work on goa…
H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark
Solim LeGris, Wai Keen Vong, Brenden M. Lake +1
The Abstraction and Reasoning Corpus (ARC) is a visual program synthesis benchmark designed to test challenging out-of-distribution generalization in humans and machines. Since 201…