5 papers
Evaluating the relationship between regularity and learnability in recursive numeral systems using Reinforcement Learning
Andrea Silvi, Ponrawee Prasertsom, Jennifer Culbertson +3
Human recursive numeral systems (i.e., counting systems such as English base-10 numerals), like many other grammatical systems, are highly regular. Following prior work that relate…
Recursive numeral systems are highly regular and easy to process
Ponrawee Prasertsom, Andrea Silvi, Jennifer Culbertson +3
Much recent work has shown how cross-linguistic variation is constrained by competing pressures from efficient communication. However, little attention has been paid to the role of…
PACE: Procedural Abstractions for Communicating Efficiently
Jonathan D. Thomas, Andrea Silvi, Devdatt Dubhashi +1
A central but unresolved aspect of problem-solving in AI is the capability to introduce and use abstractions, something humans excel at. Work in cognitive science has demonstrated…
Learning Efficient Recursive Numeral Systems via Reinforcement Learning
Andrea Silvi, Jonathan Thomas, Emil Carlsson +2
It has previously been shown that by using reinforcement learning (RL), agents can derive simple approximate and exact-restricted numeral systems that are similar to human ones (Ca…
Reasoning in Transformers -- Mitigating Spurious Correlations and Reasoning Shortcuts
Daniel Enström, Viktor Kjellberg, Moa Johansson
Transformer language models are neural networks used for a wide variety of tasks concerning natural language, including some that also require logical reasoning. However, a transfo…