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For a natural language problem that requires some non-trivial reasoning tosolve, there are at least two ways to do it using a large language model (LLM).One is to ask it to solve it directly. The other is to use it to extract thefacts from the problem text and then use a theorem prover to solve it. In thisnote, we compare the two methods using ChatGPT and GPT4 on a series of logicword puzzles, and conclude that the latter is the right approach.

Using Language Models For Knowledge Acquisition in Natural Language Reasoning Problems