Understanding the process of emotion generation is crucial for analyzing the
causes behind emotions. Causal Emotion Entailment (CEE), an
emotion-understanding task, aims to identify the causal utterances in a
conversation that stimulate the emotions expressed in a target utterance.
However, current works in CEE mainly focus on modeling semantic and emotional
interactions in conversations, neglecting the exploration of the
emotion-generation process. This hinders the models from deeply understanding
emotions, restricting their ability to produce explainable predictions. In this
work, inspired by the emotion generation process of
"stimulus-appraisal-emotion" in the cognitive appraisal theory, we introduce a
step-by-step reasoning method, Emotion-Cause Reasoning Chain (ECR-Chain), to
infer the stimulus from the target emotional expressions in conversations.
Specifically, we first introduce the ECR-Chain to ChatGPT via few-shot
prompting, which significantly improves its performance on the CEE task. We
further propose an automated construction process to utilize ChatGPT in
building an ECR-Chain set, which can enhance the reasoning abilities of smaller
models through supervised training and assist the Vicuna-7B model in achieving
state-of-the-art CEE performance. Moreover, our methods can enable these
generative language models to effectively perform emotion-cause reasoning in an
explainable manner. Our code, data and more details are at
this https URL

通过逐步推理方法 (ECR-Chain) 以及促进模型优化的 few-shot prompting，本研究在生成式语言模型中引入情感生成过程的因果关系，并且能够在可解释的方式下成功进行情感因果推理。

ECR-Chain: 通过推理链推进生成式语言模型以提高情感原因推理

ECR-Chain: Advancing Generative Language Models to Better Emotion-Cause  Reasoners through Reasoning Chains

We present THOUGHTSCULPT, a general reasoning and search method for tasks
with outputs that can be decomposed into components. THOUGHTSCULPT explores a
search tree of potential solutions using Monte Carlo Tree Search (MCTS),
building solutions one action at a time and evaluating according to any
domain-specific heuristic, which in practice is often simply an LLM evaluator.
Critically, our action space includes revision actions: THOUGHTSCULPT may
choose to revise part of its previous output rather than continuing to build
the rest of its output. Empirically, THOUGHTSCULPT outperforms state-of-the-art
reasoning methods across three challenging tasks: Story Outline Improvement (up
to +30% interestingness), Mini-Crosswords Solving (up to +16% word success
rate), and Constrained Generation (up to +10% concept coverage).

THOUGHTSCULPT 是一种通用的推理和搜索方法，采用蒙特卡洛树搜索（Monte Carlo Tree Search）来构建解决方案，其中的行动空间包括修订操作，并在三个具有挑战性的任务中表现优于现有的推理方法。

思维雕塑：通过中间修订和搜索进行推理

THOUGHTSCULPT: Reasoning with Intermediate Revision and Search

Recent work has made a preliminary attempt to use large language models
(LLMs) to solve the stance detection task, showing promising results. However,
considering that stance detection usually requires detailed background
knowledge, the vanilla reasoning method may neglect the domain knowledge to
make a professional and accurate analysis. Thus, there is still room for
improvement of LLMs reasoning, especially in leveraging the generation
capability of LLMs to simulate specific experts (i.e., multi-agents) to detect
the stance. In this paper, different from existing multi-agent works that
require detailed descriptions and use fixed experts, we propose a Dynamic
Experienced Expert Modeling (DEEM) method which can leverage the generated
experienced experts and let LLMs reason in a semi-parametric way, making the
experts more generalizable and reliable. Experimental results demonstrate that
DEEM consistently achieves the best results on three standard benchmarks,
outperforms methods with self-consistency reasoning, and reduces the bias of
LLMs.

使用大型语言模型和动态经验专家建模方法（DEEM）改进 LLMs 推理能力，实现更广泛和可靠的多方位分析，在三个标准基准测试中取得最佳结果并减少 LLMs 的偏差。