human
Rewrite the paragraph below.
The rules in RPMs for the RAVEN/I-RAVEN datasets are applied row-wise. However, these rules can exist in either rows or columns. We evaluate the performance on two different setups to determine if our model can discover the rule embeddings with no prior knowledge of whether the rules were applied row-wise or column-wise. In our first setup, we train \model with the prior knowledge of row-wise rules in RPMs. We train our second model by preparing rule embeddings on both rows and columns and finally concatenating them to predict the correct answer. Our model performance drop was only $3.5 \%$ from $98.3 \%$ to $94.8 \%$. This indicates that our model is capable of ignoring the distraction from the column-wise rule application.
gpt
--- Reasoning ---
1. Is the question multiple-choice question?
No
2. Is it implied multiple-choice question?
No
3. If two experts were independently asked the question, will the final answer be exactly the same?
No

--- Answer ---
open