Recent works show that the largest of the large language models (LLMs) can solve many simple reasoning tasks expressed in natural language, without any/much supervision. But, can they also solve challenging first-order combinatorial reasoning problems, such as graph coloring, knapsack and cryptarithmetic? To answer this question, we present PuzzleBench, a dataset of 31 such challenging problems along with a few solved instances for each problem. These problems are all first order, i.e., they can be instantiated with problem instances of varying sizes, and most of them are NP-hard, requiring several reasoning steps to reach the solution. We first observe that LLMs, even when aided by symbolic solvers, perform rather poorly on our dataset. In response, we propose a new approach, Puzzle-LM, which combines LLMs with both symbolic solvers and program interpreters, along with feedback from solved examples, to achieve huge performance gains. Our extensive experimentation and analyses offer new insights into the reasoning abilities and limitations of present-day LLMs.

翻译：暂无翻译