77 citations · 479 across the 36 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…
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…
Compositional learning of functions in humans and machines
Yanli Zhou, Brenden M. Lake, Adina Williams
The ability to learn and compose functions is foundational to efficient learning and reasoning in humans, enabling flexible generalizations such as creating new dishes from known c…
Improving Systematic Generalization Through Modularity and Augmentation
Laura Ruis, Brenden Lake
Systematic generalization is the ability to combine known parts into novel meaning; an important aspect of efficient human learning, but a weakness of neural network learning. In t…
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning
Maxwell Nye, Michael Henry Tessler, Joshua B. Tenenbaum +1
Human reasoning can often be understood as an interplay between two systems: the intuitive and associative ("System 1") and the deliberative and logical ("System 2"). Neural sequen…
Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others
Kanishk Gandhi, Gala Stojnic, Brenden M. Lake +1
To achieve human-like common sense about everyday life, machine learning systems must understand and reason about the goals, preferences, and actions of other agents in the environ…
CURI: A Benchmark for Productive Concept Learning Under Uncertainty
Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel +2
Humans can learn and reason under substantial uncertainty in a space of infinitely many concepts, including structured relational concepts ("a scene with objects that have the same…
Learning Task-General Representations with Generative Neuro-Symbolic Modeling
Reuben Feinman, Brenden M. Lake
People can learn rich, general-purpose conceptual representations from only raw perceptual inputs. Current machine learning approaches fall well short of these human standards, alt…
Learning Compositional Rules via Neural Program Synthesis
Maxwell I. Nye, Armando Solar-Lezama, Joshua B. Tenenbaum +1
Many aspects of human reasoning, including language, require learning rules from very little data. Humans can do this, often learning systematic rules from very few examples, and c…
The Omniglot challenge: a 3-year progress report
Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum
Three years ago, we released the Omniglot dataset for one-shot learning, along with five challenge tasks and a computational model that addresses these tasks. The model was not mea…