Papers by Yi-Chien Lin
Surprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorly (2026.eacl-short)
Copied to clipboard
| Challenge: | Recent work has observed an inverse scaling relationship between Transformers’ per-word estimated probability and the predictive power of their surprisal estimates on reading times. |
| Approach: | They conducted a more comprehensive evaluation using surprisal estimates from 17 pre-trained LMs on two functional magnetic resonance imaging datasets. |
| Outcome: | Recent work shows that surprisal from larger Transformer-based models is less predictive of reading times, resolving the inconclusive results and indicating that this trend is not specific to latency-based measures. |