Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?
AbstractThis work presents a linguistic analysis into why larger Transformer-based pre-trained language models with more parameters and lower perplexity nonetheless yield surprisal estimates that are less predictive of human reading times. First, regression analyses show a strictly m...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
The MIT Press
2023-01-01
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| Series: | Transactions of the Association for Computational Linguistics |
| Online Access: | https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00548/115371/Why-Does-Surprisal-From-Larger-Transformer-Based |