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

Reasoning over natural language is a challenging problem in NLP. In this
work, we focus on proof generation: Given a hypothesis and a set of supporting
facts, the model generates a proof tree indicating how to derive the hypothesis
from supporting facts. Compared to generating the entire proof in one shot,
stepwise generation can better exploit the compositionality and generalize to
longer proofs but has achieved limited success on real-world data. Existing
stepwise methods struggle to generate proof steps that are both logically valid
and relevant to the hypothesis. Instead, they tend to hallucinate invalid steps
given the hypothesis. In this paper, we present a novel stepwise method,
NLProofS (Natural Language Proof Search), which learns to generate relevant
steps conditioning on the hypothesis. At the core of our approach, we train an
independent verifier to check the validity of the proof steps to prevent
hallucination. Instead of generating steps greedily, we search for proofs
maximizing a global proof score judged by the verifier. NLProofS achieves
state-of-the-art performance on EntailmentBank and RuleTaker. Specifically, it
improves the correctness of predicted proofs from 27.7% to 33.3% in the
distractor setting of EntailmentBank, demonstrating the effectiveness of
NLProofS in generating challenging human-authored proofs.

本文介绍了一种新的逐步方法 NLProofS，通过在给定假设的条件下学习生成相关步骤，从而在 NLP 中解决证明生成的问题，并在 EntailmentBank 上取得了最先进的性能。

使用验证器引导搜索生成自然语言证明

Generating Natural Language Proofs with Verifier-Guided Search

Our goal, in the context of open-domain textual question-answering (QA), is
to explain answers by showing the line of reasoning from what is known to the
answer, rather than simply showing a fragment of textual evidence (a
"rationale'"). If this could be done, new opportunities for understanding and
debugging the system's reasoning become possible. Our approach is to generate
explanations in the form of entailment trees, namely a tree of multipremise
entailment steps from facts that are known, through intermediate conclusions,
to the hypothesis of interest (namely the question + answer). To train a model
with this skill, we created ENTAILMENTBANK, the first dataset to contain
multistep entailment trees. Given a hypothesis (question + answer), we define
three increasingly difficult explanation tasks: generate a valid entailment
tree given (a) all relevant sentences (b) all relevant and some irrelevant
sentences, or (c) a corpus. We show that a strong language model can partially
solve these tasks, in particular when the relevant sentences are included in
the input (e.g., 35% of trees for (a) are perfect), and with indications of
generalization to other domains. This work is significant as it provides a new
type of dataset (multistep entailments) and baselines, offering a new avenue
for the community to generate richer, more systematic explanations.

通过生成包含多前提蕴含步骤（即知识事实到问题答案的中间推理结论）的蕴含树形式的解释，本研究提出第一个含有多步蕴含树的数据集 ENTAILMENTBANK，用于培训模型执行三项解释任务。结果表明，当输入包含相关句子时（例如，任务（a）的 35% 结果完美），强大的语言模型能部分地解决这些任务，并具有推广到其他领域的迹象。