Individual results

View docs

View in-depth performance of a single language model on a single test suite.

Region-by-region surprisal
Sample item for Filler-Gap Dependencies (object extraction)
Item
Condition
prefix comp np1 verb np2 prep np3 end
Item Condition prefix comp np1 verb np2 prep np3 end
1 what_nogap I know what our uncle grabbed the food in front of the guests at the holiday party
1 that_nogap I know that our uncle grabbed the food in front of the guests at the holiday party
1 what_gap I know what our uncle grabbed in front of the guests at the holiday party
1 that_gap I know that our uncle grabbed in front of the guests at the holiday party
Prediction performance for TinyLSTM on Filler-Gap Dependencies (object extraction)
Accuracy
Formula
Description
AccuracyPredictionDescription
83.33% (642,what_gap/6,prep) < (639,that_gap/6,prep) We expect the “prep” region to be lower in the what_gap condition than in the that_gap condition, because gaps must be licensed by upstream wh words (such as “what”).
100.00% (640,what_nogap/5,np2) > (641,that_nogap/5,np2) We expect the NP2 to be less surprising in the that_no-gap condition than in the what_no-gap condition, because an upstream wh-word should set up an expectation for a gap.