揭示大模型情感分析的不确定性根源与应对策略
An overview of model uncertainty and variability in LLM-based sentiment analysis. Challenges, mitigation strategies and the role of explainability
- 分析大模型在情感判断中因随机推理、提示敏感等导致的不一致问题
- 发现温度参数显著增加输出随机性，影响结果稳定性
- 强调可解释性对提升信任和可靠性的关键作用，适合高风险场景使用
大语言模型（LLMs）虽显著推进了情感分析，但其内在的不确定性与变异性带来了可靠性和一致性挑战。本文系统探讨了基于大模型的情感分析中的模型变异性问题（MVP），表现为分类结果不一致、观点极化及推断不确定性，源于随机推理机制、提示敏感性以及训练数据中的偏见。通过案例分析与实例展示，揭示了其实际影响。研究进一步探讨核心挑战与缓解策略，特别关注温度参数对输出随机性的驱动作用，并强调可解释性在增强透明度与用户信任中的关键角色。本研究为提升情感分析的稳定性、可复现性与可信度提供了结构化视角，有助于推动其在金融、医疗、政策制定等高风险领域的应用。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have significantly advanced sentiment analysis, yet their inherent uncertainty and variability pose critical challenges to achieving reliable and consistent outcomes. This paper systematically explores the Model Variability Problem (MVP) in LLM-based sentiment analysis, characterized by inconsistent sentiment classification, polarization, and uncertainty arising from stochastic inference mechanisms, prompt sensitivity, and biases in training data. We analyze the core causes of MVP, presenting illustrative examples and a case study to highlight its impact. In addition, we investigate key challenges and mitigation strategies, paying particular attention to the role of temperature as a driver of output randomness and emphasizing the crucial role of explainability in improving transparency and user trust. By providing a structured perspective on stability, reproducibility, and trustworthiness, this study helps develop more reliable, explainable, and robust sentiment analysis models, facilitating their deployment in high-stakes domains such as finance, healthcare, and policymaking, among others.
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