42 citations · 157 across the 18 of their papers we have counts for
6 papers · 1 filter
CiwaGAN: Articulatory information exchange
Gašper Beguš, Thomas Lu, Alan Zhou +2
Humans encode information into sounds by controlling articulators and decode information from sounds using the auditory apparatus. This paper introduces CiwaGAN, a model of human s…
Large language models and (non-)linguistic recursion
Maksymilian Dąbkowski, Gašper Beguš
Recursion is one of the hallmarks of human language. While many design features of language have been shown to exist in animal communication systems, recursion has not. Previous re…
Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks
Gašper Beguš, Thomas Lu, Zili Wang
Computational models of syntax are predominantly text-based. Here we propose that the most basic first step in the evolution of syntax can be modeled directly from raw speech in a…
Large Linguistic Models: Investigating LLMs' metalinguistic abilities
Gašper Beguš, Maksymilian Dąbkowski, Ryan Rhodes
The performance of large language models (LLMs) has recently improved to the point where models can perform well on many language tasks. We show here that--for the first time--the…
AI-assisted coding: Experiments with GPT-4
Russell A Poldrack, Thomas Lu, Gašper Beguš
Artificial intelligence (AI) tools based on large language models have acheived human-level performance on some computer programming tasks. We report several experiments using GPT-…
Approaching an unknown communication system by latent space exploration and causal inference
Gašper Beguš, Andrej Leban, Shane Gero
This paper proposes a methodology for discovering meaningful properties in data by exploring the latent space of unsupervised deep generative models. We combine manipulation of ind…