Large language models (LLMs) have shown remarkable reasoning capabilities given chain-of-thought prompts (examples with intermediate reasoning steps). Existing benchmarks measure reasoning ability indirectly, by evaluating accuracy on downstream tasks such as mathematical reasoning. However, it is unclear how these models obtain the answers and whether they rely on simple heuristics rather than the generated chain-of-thought. To enable systematic exploration of the reasoning ability of LLMs, we present a new synthetic question-answering dataset called PrOntoQA, where each example is generated from a synthetic world model represented in first-order logic. This allows us to parse the generated chain-of-thought into symbolic proofs for formal analysis. Our analysis on InstructGPT and GPT-3 shows that LLMs are quite capable of making correct individual deduction steps, and so are generally capable of reasoning, even in fictional contexts. However, they have difficulty with proof planning: When multiple valid deduction steps are available, they are not able to systematically explore the different options.

翻译：大语言模型（LLMs）在思维链提示（包含中间推理步骤的示例）下展现出卓越的推理能力。现有基准测试通过评估数学推理等下游任务的准确性来间接衡量推理能力，但尚不清楚这些模型如何获得答案，以及它们是否依赖简单启发式方法而非生成的思维链。为了系统探索LLMs的推理能力，我们提出了一个名为PrOntoQA的新型合成问答数据集，其中每个示例均基于一阶逻辑表示的合成世界模型生成。这使得我们能够将生成的思维链解析为符号证明以进行形式化分析。对InstructGPT和GPT-3的分析表明，LLMs在做出正确的独立推理步骤方面相当出色，因此即使在虚构语境中也普遍具备推理能力。然而，它们在证明规划方面存在困难：当存在多个有效推理步骤时，模型无法系统性地探索不同选项。