Abstract
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.
Abstract (translated)
大型语言模型（LLMs）在情感分析方面取得了显著进展，但其内在的不确定性和变化性给实现可靠和一致的结果带来了重大挑战。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（Model Variability Problem, MVP），该问题表现为不一致的情感分类、极化以及由于随机推理机制、提示敏感性及训练数据偏差所产生的不确定性。我们分析了MVP的核心原因，并通过示例和案例研究来强调其影响。此外，本文还探讨了关键挑战和缓解策略，特别关注温度作为输出随机性的驱动因素的作用，并强调可解释性在提高透明度和用户信任中的关键作用。通过提供关于稳定性、重现性和可信度的结构化视角，本研究有助于开发更可靠、更具解释性和鲁棒性的模型，从而促进这些模型在金融、医疗保健及政策制定等高风险领域的部署。
URL
https://arxiv.org/abs/2504.04462
