Logical reasoning remains a pivotal component within the realm of artificial
intelligence. The recent evolution of large language models (LLMs) has marked
significant progress in this domain. The adoption of strategies like
chain-of-thought (CoT) has enhanced the performance of LLMs across diverse
reasoning tasks. Nonetheless, logical reasoning that involves proof planning,
specifically those that necessitate the validation of explanation accuracy,
continues to present stumbling blocks. In this study, we first evaluate the
efficacy of LLMs with advanced CoT strategies concerning such tasks. Our
analysis reveals that LLMs still struggle to navigate complex reasoning chains,
which demand the meticulous linkage of premises to derive a cogent conclusion.
To address this issue, we finetune a smaller-scale language model, equipping it
to decompose proof objectives into more manageable subgoals. We also introduce
contrastive decoding to stepwise proof generation, making use of negative
reasoning paths to strengthen the model's capacity for logical deduction.
Experiments on EntailmentBank underscore the success of our method in
augmenting the proof planning abilities of language models.

通过引入先进的思维链接策略和分步证明生成对小规模语言模型进行微调，增强了逻辑推理能力，成功提升了语言模型完成复杂推理任务的效果。

LLM 是否是严格的逻辑推理者？通过对比逐步解码增强自然语言证明生成

Are LLMs Rigorous Logical Reasoner? Empowering Natural Language Proof  Generation with Contrastive Stepwise Decoding

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.

本文通过介绍一种新的合成问答数据集 PrOntoQA，旨在通过对 LLMs 的系统探索，该数据集是通过使用一阶逻辑表示的合成世界模型生成的。作者对 InstructGPT 和 GPT-3 进行了分析，表明 LLMs 能够进行正确的逻辑推理，但在方案规划方面存在困​​难。