基于LLM的情感分析中模型不确定性与变异性的综述：挑战、缓解策略及可解释性的作用
An overview of model uncertainty and variability in LLM-based sentiment analysis. Challenges, mitigation strategies and the role of explainability
AI总结：
本文系统综述了基于LLM的情感分析中模型变异性问题（MVP）的成因与影响，重点分析温度参数和可解释性的作用，并提出缓解策略以提升模型在金融、医疗等高风险领域的稳定性、可复现性与可信度。
摘要：
大型语言模型（LLMs）显著推动了情感分析的发展，然而其固有的不确定性和变异性对实现可靠且一致的结果构成了关键挑战。本文系统地探讨了基于LLM的情感分析中的模型变异性问题（MVP），其特征是由随机推理机制、提示敏感性和训练数据偏差所引起的情感分类不一致、极化以及不确定性。我们分析了MVP的核心成因，并给出了说明性示例和一个案例研究以突出其影响。此外，我们研究了关键挑战与缓解策略，特别关注温度作为输出随机性驱动因素的作用，并强调可解释性在提升透明度和用户信任方面的关键作用。通过提供关于稳定性、可复现性和可信度的结构化视角，本研究有助于开发更可靠、可解释且鲁棒的情感分析模型，促进其在金融、医疗保健和政策制定等高风险领域的部署。
英文摘要：
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.
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。
