链式思维对情感分析帮助有限，模型主要依赖示例信息
Reassessing the Role of Chain-of-Thought in Sentiment Analysis: Insights and Limitations
- 用链式思维提示激发推理，观察其对情感理解的影响
- 链式思维提示下，模型仍聚焦于方面词而非情感极性
- 实验表明模型依赖示例信息，推理作用微乎其微
语言与思维的关系仍是未解的哲学问题。在大语言模型背景下，这一争论引出关键问题：语言模型对语义的理解是否依赖于思维过程？为探究此问题，本文研究推理技术能否促进语义理解。具体地，将思维概念化为推理，采用链式思维提示作为推理手段，考察其在情感分析任务中的影响。实验显示，链式思维对情感分析任务影响极小；标准与链式思维提示均使生成内容聚焦于方面词而非情感极性。此外，反事实实验表明，模型处理情感任务主要依赖示范信息。结果支持语言与思维独立的观点。
原文摘要 · Abstract (English)
The relationship between language and thought remains an unresolved philosophical issue. Existing viewpoints can be broadly categorized into two schools: one asserting their independence, and another arguing that language constrains thought. In the context of large language models, this debate raises a crucial question: Does a language model's grasp of semantic meaning depend on thought processes? To explore this issue, we investigate whether reasoning techniques can facilitate semantic understanding. Specifically, we conceptualize thought as reasoning, employ chain-of-thought prompting as a reasoning technique, and examine its impact on sentiment analysis tasks. The experiments show that chain-of-thought has a minimal impact on sentiment analysis tasks. Both the standard and chain-of-thought prompts focus on aspect terms rather than sentiment in the generated content. Furthermore, counterfactual experiments reveal that the model's handling of sentiment tasks primarily depends on information from demonstrations. The experimental results support the first viewpoint.
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