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Context: For higher depths (3+), on the three datasets SI produces predictions that are close to the majority class prediction. We find that it tends to over-predict Disproved in the binary case and Unknown in the three-way classification case (cf. Appendix B.3), making it perform even worse than the majority class for Depth-5 of PrOntoQA which has more Proved labels than Disproved. However, we surprisingly observe that the performance of CoT remains relatively high for the ProofWriter-PD dataset, and the accuracy does not diminish. In the next sub-section, we verify the reason for this behaviour of CoT. For Fact Check, we randomly selected 100 examples from the Depth-0 examples.For Rule Selection, we randomly selected 100 examples and manually enumerated every rule whose consequent unifies with the goal. A model prediction is considered correct if it predicts all such rules correctly. For Goal Decomposition, we randomly selected 100 rules and goals such that the consequent of the rule unifies with the goal and then manually wrote the sub-goals. A model prediction is considered correct if it predicts all the sub-goals correctly. For Sign Agreement, we re-used the same examples from the Goal Decomposition module and manually labeled them with respect to their sign agreement/disagreement.

Question: Are the results similar for other variants of values, given test set has only 1000 examples?
Yes, the results are similar for other variants of values.