推理还是过度思考：评估大语言模型在金融情感分析中的表现
Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis
AI总结：
本文在金融情感分析任务上评估多种大语言模型，发现推理或思维链提示并未提升性能，反而可能引发过度思考，GPT-4o无思维链时表现最佳，表明系统1式直觉思维更契合人类判断。
摘要：
我们研究了大语言模型（LLMs）在零样本金融情感分析中的有效性，涵盖基于推理的模型和非推理模型。利用由领域专家标注的Financial PhraseBank数据集，我们评估了各种LLMs和提示策略在金融语境下与人工标注情感的吻合程度。我们在模拟系统1（快速且直觉）或系统2（缓慢且审慎）思维的不同提示范式下，比较了三种专有大语言模型（GPT-4o、GPT-4.1、o3-mini），并以两个在金融情感分析上微调的较小模型（FinBERT-Prosus、FinBERT-Tone）作为基准。我们的研究结果表明，无论是通过提示还是模型固有的设计引入推理，都不会提升该任务上的表现。令人意外的是，最准确且与人类判断最一致的模型与方法组合是未使用任何思维链（CoT）提示的GPT-4o。我们进一步探讨了语言复杂性和标注一致性水平对性能的影响，发现推理可能引入过度思考，从而导致次优预测。这表明，对于金融情感分类而言，快速、直觉的“系统1”式思维比由推理模型或思维链提示所模拟的“系统2”式缓慢、审慎的推理更贴近人类判断。我们的结果挑战了“更多推理总能带来更好的LLM决策”这一默认假设，尤其是在高风险的金融应用中。
英文摘要：
We investigate the effectiveness of large language models (LLMs), including reasoning-based and non-reasoning models, in performing zero-shot financial sentiment analysis. Using the Financial PhraseBank dataset annotated by domain experts, we evaluate how various LLMs and prompting strategies align with human-labeled sentiment in a financial context. We compare three proprietary LLMs (GPT-4o, GPT-4.1, o3-mini) under different prompting paradigms that simulate System 1 (fast and intuitive) or System 2 (slow and deliberate) thinking and benchmark them against two smaller models (FinBERT-Prosus, FinBERT-Tone) fine-tuned on financial sentiment analysis. Our findings suggest that reasoning, either through prompting or inherent model design, does not improve performance on this task. Surprisingly, the most accurate and human-aligned combination of model and method was GPT-4o without any Chain-of-Thought (CoT) prompting. We further explore how performance is impacted by linguistic complexity and annotation agreement levels, uncovering that reasoning may introduce overthinking, leading to suboptimal predictions. This suggests that for financial sentiment classification, fast, intuitive "System 1"-like thinking aligns more closely with human judgment compared to "System 2"-style slower, deliberative reasoning simulated by reasoning models or CoT prompting. Our results challenge the default assumption that more reasoning always leads to better LLM decisions, particularly in high-stakes financial applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
