The impact of non-deterministic outputs from Large Language Models (LLMs) is not well examined for financial text understanding tasks. Through a compelling case study on investing in the US equity market via news sentiment analysis, we uncover substantial variability in sentence-level sentiment classification results, underscoring the innate volatility of LLM outputs. These uncertainties cascade downstream, leading to more significant variations in portfolio construction and return. While tweaking the temperature parameter in the language model decoder presents a potential remedy, it comes at the expense of stifled creativity. Similarly, while ensembling multiple outputs mitigates the effect of volatile outputs, it demands a notable computational investment. This work furnishes practitioners with invaluable insights for adeptly navigating uncertainty in the integration of LLMs into financial decision-making, particularly in scenarios dictated by non-deterministic information.

翻译：大型语言模型（LLMs）的非确定性输出对金融文本理解任务的影响尚未得到充分研究。通过对美国股市投资中基于新闻情感分析的案例研究，我们揭示了句子级情感分类结果的显著变异性，凸显了LLM输出的固有波动性。这些不确定性会向下游传导，导致投资组合构建和收益出现更大波动。虽然调整语言模型解码器中的温度参数是一种潜在补救措施，但这会抑制创造性。类似地，尽管集成多个输出能缓解输出波动的影响，却需要大量的计算投入。本研究为从业者提供了将LLM整合到金融决策中时有效应对不确定性的宝贵见解，尤其是在非确定性信息主导的场景下。