Zobrazeno 1 - 10
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pro vyhledávání: '"Millière, A."'
Autor:
Millière, Raphaël
This chapter critically examines the potential contributions of modern language models to theoretical linguistics. Despite their focus on engineering goals, these models' ability to acquire sophisticated linguistic knowledge from mere exposure to dat
Externí odkaz:
http://arxiv.org/abs/2408.07144
Autor:
Millière, Raphaël, Rathkopf, Charles
Evaluating the cognitive capacities of large language models (LLMs) requires overcoming not only anthropomorphic but also anthropocentric biases. This article identifies two types of anthropocentric bias that have been neglected: overlooking how auxi
Externí odkaz:
http://arxiv.org/abs/2407.03859
Analogical reasoning is considered core to human learning and cognition. Recent studies have compared the analogical reasoning abilities of human subjects and Large Language Models (LLMs) on abstract symbol manipulation tasks, such as letter string a
Externí odkaz:
http://arxiv.org/abs/2406.13803
Autor:
Millière, Raphaël
Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural
Externí odkaz:
http://arxiv.org/abs/2405.04048
Autor:
Millière, Raphaël, Buckner, Cameron
In this paper, the second of two companion pieces, we explore novel philosophical questions raised by recent progress in large language models (LLMs) that go beyond the classical debates covered in the first part. We focus particularly on issues rela
Externí odkaz:
http://arxiv.org/abs/2405.03207
Autor:
Millière, Raphaël, Buckner, Cameron
Large language models like GPT-4 have achieved remarkable proficiency in a broad spectrum of language-based tasks, some of which are traditionally associated with hallmarks of human intelligence. This has prompted ongoing disagreements about the exte
Externí odkaz:
http://arxiv.org/abs/2401.03910
Autor:
Millière, Raphaël, author
Publikováno v:
Philosophical Perspectives on Psychedelic Psychiatry, 2024, ill.
Externí odkaz:
https://doi.org/10.1093/oso/9780192898371.003.0002
Autor:
Millière, Raphaël
A core challenge in the development of increasingly capable AI systems is to make them safe and reliable by ensuring their behaviour is consistent with human values. This challenge, known as the alignment problem, does not merely apply to hypothetica
Externí odkaz:
http://arxiv.org/abs/2311.02147
Large language models (LLMs) exhibit remarkable performance improvement through in-context learning (ICL) by leveraging task-specific examples in the input. However, the mechanisms behind this improvement remain elusive. In this work, we investigate
Externí odkaz:
http://arxiv.org/abs/2310.00313
The remarkable performance of large language models (LLMs) on complex linguistic tasks has sparked a lively debate on the nature of their capabilities. Unlike humans, these models learn language exclusively from textual data, without direct interacti
Externí odkaz:
http://arxiv.org/abs/2304.01481