Beinborn, Lisa. creator author. aut http://id.loc.gov/vocabulary/relators/aut Hollenstein, Nora. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access XI, 158 p. 3 illus., 2 illus. in color. online resource. This book explores the cognitive plausibility of computational language models and why it’s an important factor in their development and evaluation. The authors present the idea that more can be learned about cognitive plausibility of computational language models by linking signals of cognitive processing load in humans to interpretability methods that allow for exploration of the hidden mechanisms of neural models. The book identifies limitations when applying the existing methodology for representational analyses to contextualized settings and critiques the current emphasis on form over more grounded approaches to modeling language. The authors discuss how novel techniques for transfer and curriculum learning could lead to cognitively more plausible generalization capabilities in models. The book also highlights the importance of instance-level evaluation and includes thorough discussion of the ethical considerations that may arise throughout the various stages of cognitive plausibility research. . Acknowledgments -- Chapter 1 Introduction -- Chapter 2 Foundations of Language Modeling -- Chapter 3 Cognitive Signals of Language Processing -- Chapter 4 Behavioral Patterns -- Chapter 5 Representational Structure -- Chapter 6 Procedural Strategies -- Chapter 7 Towards Cognitively More Plausible Models -- Bibliography. . by Lisa Beinborn, Nora Hollenstein. Natural language processing (Computer science) Machine learning Artificial intelligence Computational linguistics Cognitive science Applied linguistics Natural Language Processing (NLP) Machine Learning Artificial Intelligence Computational Linguistics Cognitive Science Applied Linguistics QA76.9.N38 9783031432606 https://doi.org/10.1007/978-3-031-43260-6 https://doi.org/10.1007/978-3-031-43260-6 231030 20251030110827.0 978-3-031-43260-6