arXiv 2507.003892025-07-01cs.CL

基于大语言模型的隐式情感分析因果提示方法

Causal Prompting for Implicit Sentiment Analysis with Large Language Models

School of Computing Technologies, RMIT University（RMIT大学计算机技术学院）；Institute of Innovation, Science and Sustainability, Federation University Australia（联邦大学澳大利亚创新、科学与可持续性研究所）；CSIRO’s Data61（CSIRO数据61）

Jing Ren, Wenhao Zhou, Bowen Li, Mujie Liu, Nguyen Linh Dan Le, Jiade Cen, Liping Chen, Ziqi Xu, Xiwei Xu, Xiaodong Li

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AI总结：

本文提出CAPITAL因果提示框架，通过前门调整分解CoT推理中的因果效应并结合对比学习，显著提升大语言模型在隐式情感分析中的准确性与鲁棒性。

摘要：

隐式情感分析（ISA）旨在推断隐含而非明确表达的情感，要求模型对微妙的语境线索进行更深层次的推理。虽然近期使用大语言模型（LLMs）的基于提示的方法在ISA中展现出潜力，但它们通常依赖对思维链（CoT）推理路径的多数投票，而不评估其因果有效性，使其容易受到内部偏差和虚假相关性的影响。为应对这一挑战，我们提出CAPITAL，一个将前门调整纳入CoT推理的因果提示框架。CAPITAL将整体因果效应分解为两个部分：输入提示对推理链的影响，以及这些推理链对最终输出的影响。这些部分使用基于编码器的聚类和NWGM近似进行估计，并采用对比学习目标使编码器的表示与LLM的推理空间更好地对齐。在基准ISA数据集上使用三个LLMs进行的实验表明，CAPITAL在准确性和鲁棒性方面均持续优于强提示基线，尤其是在对抗条件下。这项工作为将因果推断整合到LLM提示中提供了一种有原则的方法，并凸显了其在偏差感知情感推理方面的优势。源代码和案例研究可在以下地址获取：https://github.com/whZ62/CAPITAL。

英文摘要：

Implicit Sentiment Analysis (ISA) aims to infer sentiment that is implied rather than explicitly stated, requiring models to perform deeper reasoning over subtle contextual cues. While recent prompting-based methods using Large Language Models (LLMs) have shown promise in ISA, they often rely on majority voting over chain-of-thought (CoT) reasoning paths without evaluating their causal validity, making them susceptible to internal biases and spurious correlations. To address this challenge, we propose CAPITAL, a causal prompting framework that incorporates front-door adjustment into CoT reasoning. CAPITAL decomposes the overall causal effect into two components: the influence of the input prompt on the reasoning chains, and the impact of those chains on the final output. These components are estimated using encoder-based clustering and the NWGM approximation, with a contrastive learning objective used to better align the encoder's representation with the LLM's reasoning space. Experiments on benchmark ISA datasets with three LLMs demonstrate that CAPITAL consistently outperforms strong prompting baselines in both accuracy and robustness, particularly under adversarial conditions. This work offers a principled approach to integrating causal inference into LLM prompting and highlights its benefits for bias-aware sentiment reasoning. The source code and case study are available at: https://github.com/whZ62/CAPITAL.

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