Papers by Shisen Yue
Frequency Explains the Inverse Correlation of Large Language Models’ Size, Training Data Amount, and Surprisal’s Fit to Reading Times (2024.eacl-long)
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| Challenge: | Recent studies have shown that as Transformer-based language models become larger and are trained on very large amounts of data, the fit of their surprisal estimates to naturalistic human reading times degrades. |
| Approach: | They present a series of analyses showing that word frequency is a key explanatory factor underlying these two trends. |
| Outcome: | The results show that word frequency is a key explanatory factor underlying these two trends. |