7 citations · 23 across the 11 of their papers we have counts for
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cs.CL2022★ 6 cited
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?
Byung-Doh Oh, William Schuler
This work presents a detailed linguistic analysis into why larger Transformer-based pre-trained language models with more parameters and lower perplexity nonetheless yield surprisa…
cs.CL2022★ 3 cited
Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal
Byung-Doh Oh, William Schuler
Transformer-based large language models are trained to make predictions about the next word by aggregating representations of previous tokens through their self-attention mechanism…