Large language models (LLMs) suffer from serious unfaithful chain-of-thought
(CoT) issues. Previous work attempts to measure and explain it but lacks
in-depth analysis within CoTs and does not consider the interactions among all
reasoning components jointly. In this paper, we first study the CoT
faithfulness issue at the granularity of CoT steps, identify two reasoning
paradigms: centralized reasoning and distributed reasoning, and find their
relationship with faithfulness. Subsequently, we conduct a joint analysis of
the causal relevance among the context, CoT, and answer during reasoning. The
result proves that, when the LLM predicts answers, it can recall correct
information missing in the CoT from the context, leading to unfaithfulness
issues. Finally, we propose the inferential bridging method to mitigate this
issue, in which we use the attribution method to recall information as hints
for CoT generation and filter out noisy CoTs based on their semantic
consistency and attribution scores. Extensive experiments demonstrate that our
approach effectively alleviates the unfaithful CoT problem.

大型语言模型（LLMs）存在严重的不忠实的思维链 (CoT) 问题。本文首先从 CoT 步骤的粒度研究 CoT 忠实性问题，确定了集中式推理和分布式推理两种推理范式，并发现它们与忠实性的关系。随后，我们对环境、CoT 和答案之间的因果关系进行了联合分析。结果证明，当 LLM 预测答案时，它可以从上下文中回忆起 CoT 中缺失的正确信息，导致不忠实的问题。最后，我们提出了推理桥接方法来缓解这个问题，其中我们使用归因方法来作为 CoT 生成的提示并根据其语义一致性和归因分数过滤噪声 CoTs。大量实验证明我们的方法有效地减轻了不忠实的 CoT 问题。

走向忠实的思维链条：大型语言模型正在架设推理桥梁

Towards Faithful Chain-of-Thought: Large Language Models are Bridging  Reasoners

Event reasoning is a fundamental ability that underlies many applications. It
requires event schema knowledge to perform global reasoning and needs to deal
with the diversity of the inter-event relations and the reasoning paradigms.
How well LLMs accomplish event reasoning on various relations and reasoning
paradigms remains unknown. To mitigate this disparity, we comprehensively
evaluate the abilities of event reasoning of LLMs. We introduce a novel
benchmark EV2 for EValuation of EVent reasoning. EV2 consists of two levels of
evaluation of schema and instance and is comprehensive in relations and
reasoning paradigms. We conduct extensive experiments on EV2. We find that LLMs
have abilities to accomplish event reasoning but their performances are far
from satisfactory. We also notice the imbalance of event reasoning abilities in
LLMs. Besides, LLMs have event schema knowledge, however, they're not aligned
with humans on how to utilize the knowledge. Based on these findings, we
introduce two methods to guide the LLMs to utilize the event schema knowledge.
Both methods achieve improvements.

对 LLMs 的事件推理能力进行了综合评估，发现 LLMs 在事件推理方面的表现仍然不尽如人意。通过发现 LLMs 对事件推理能力存在的不平衡，提出了两种方法来指导 LLMs 利用事件模式知识，并获得了改进。

大型语言模型的事件推理综合评估

A Comprehensive Evaluation on Event Reasoning of Large Language Models

The study explores the effectiveness of the Chain-of-Thought approach, known
for its proficiency in language tasks by breaking them down into sub-tasks and
intermediate steps, in improving vision-language tasks that demand
sophisticated perception and reasoning. We present the "Description then
Decision" strategy, which is inspired by how humans process signals. This
strategy significantly improves probing task performance by 50%, establishing
the groundwork for future research on reasoning paradigms in complex
vision-language tasks.

该研究通过将复杂的视觉语言任务拆分为子任务和中间步骤的思维链方法，探究其在提高需要复杂感知和推理的视觉语言任务中的有效性。我们提出了 “先描述再决策” 的策略，该策略受人类信号处理方式启发，显著提高探索任务性能 50%，为进一步研究复杂视觉语言任务中的推理范式奠定了基础。