Chain of thought finetuning aims to endow small student models with reasoning
capacity to improve their performance towards a specific task by allowing them
to imitate the reasoning procedure of large language models (LLMs) beyond
simply predicting the answer to the question. However, the existing methods 1)
generate rationale before the answer, making their answer correctness sensitive
to the hallucination in the rationale;2) force the student model to repeat the
exact LLMs rationale expression word-after-word, which could have the model
biased towards learning the expression in rationale but count against the model
from understanding the core logic behind it. Therefore, we propose a robust
Post-Semantic-Thinking (PST) strategy to generate answers before rationale.
Thanks to this answer-first setting, 1) the answering procedure can escape from
the adverse effects caused by hallucinations in the rationale; 2) the complex
reasoning procedure is tightly bound with the relatively concise answer, making
the reasoning for questions easier with the prior information in the answer; 3)
the efficiency of the method can also benefit from the setting since users can
stop the generation right after answers are outputted when inference is
conducted. Furthermore, the PST strategy loose the constraint against the
generated rationale to be close to the LLMs gold standard in the hidden
semantic space instead of the vocabulary space, thus making the small student
model better comprehend the semantic reasoning logic in rationale. Extensive
experiments conducted across 12 reasoning tasks demonstrate the effectiveness
of PST.

通过链式思维微调策略，我们提出了一种强大的后语义思考策略 (Post-Semantic-Thinking, PST)，旨在通过在表达方式上不受限制地生成答案，使得小型学生模型能够更好地理解基于语义推理的逻辑。广泛的实验在 12 个推理任务上证明了 PST 的有效性。

后语义思维：从大规模语言模型中提炼推理能力的强大策略

Post-Semantic-Thinking: A Robust Strategy to Distill Reasoning Capacity  from Large Language Models

Reasoning on defeasible knowledge is a topic of interest in the area of
description logics, as it is related to the need of representing exceptional
instances in knowledge bases. In this direction, in our previous works we
presented a framework for representing (contextualized) OWL RL knowledge bases
with a notion of justified exceptions on defeasible axioms: reasoning in such
framework is realized by a translation into ASP programs. The resulting
reasoning process for OWL RL, however, introduces a complex encoding in order
to capture reasoning on the negative information needed for reasoning on
exceptions. In this paper, we apply the justified exception approach to
knowledge bases in $\textit{DL-Lite}_{\cal R}$, i.e., the language underlying
OWL QL. We provide a definition for $\textit{DL-Lite}_{\cal R}$ knowledge bases
with defeasible axioms and study their semantic and computational properties.
In particular, we study the effects of exceptions over unnamed individuals. The
limited form of $\textit{DL-Lite}_{\cal R}$ axioms allows us to formulate a
simpler ASP encoding, where reasoning on negative information is managed by
direct rules. The resulting materialization method gives rise to a complete
reasoning procedure for instance checking in $\textit{DL-Lite}_{\cal R}$ with
defeasible axioms. Under consideration in Theory and Practice of Logic
Programming (TPLP).

本文基于正当例外的方法，对 DL-Lite_R 知识库进行了建模，并研究了其语义和计算特性，提出一种较简单的 ASP 编码来管理负信息，从而实现了对其带有例外公理的实例检查的完整推理过程。