Insights from Animals to Build Better Artificial Language Learners

被引：0
|
作者：
Toro, Juan Manuel [1 ,2 ,3 ]
机构：
[1] Inst Catalana Recerca & Estudis Avancats, Inst Med Genet & Appl Genom, Barcelona, Spain
[2] Univ Pompeu Fabra, Barcelona, Spain
[3] Univ Pompeu Fabra, C Ramon Trias Fargas 25-27, Barcelona 08005, Spain
来源：
COMPARATIVE COGNITION & BEHAVIOR REVIEWS | 2024年 / 19卷
关键词：
language; guided learning; artificial intelligence; LLMs;
D O I：
10.3819/CCBR.2024.190008
中图分类号：
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号：
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要：
Recent advances in artificial intelligence have produced large language models that are proficient at processing and generating language. Lessons learned from comparative cognition will be pivotal to understanding the conditions under which different cognitive systems (both natural and artificial) might discover complex linguistic structures. Such lessons will fuel the development of more efficient learning algorithms. More important, they will help to advance a new science of intelligence that takes into account human, animal, and artificial systems.
引用
收藏
页码：91 / 95
页数：5
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