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Context: These approaches break the problem into smaller modules and use separate LMs to solve each module Zhou et al. (2022b); Khot et al. (2022); Sprague et al. (2022); Zhou et al. (2022a).Most relevant to our work,in Tafjord et al. (2020), a single LM module iteratively and exhaustively derives all conclusions based on the facts and rules, and then the goal statement is checked against the final set of conclusions to confirm if it can be proved from the theory. Since exhaustively deriving all conclusions is computationally expensive, Creswell et al. (2022) consider an alternative approach with two modules: 1- selection, which, guided by the goal, selects a subset of the facts and rules from which new conclusions can be derived toward proving the goal, and 2- inference, which takes the selected facts and rules and derives a new conclusion. The two modules are called iteratively until a halting criterion is met. In this paper, we compare against the second approach.

Question: Doesn’t breaking the problem into sub problems increase computation?
Yes, breaking the problem into sub-problems increases computation.