Benchmarking Large Language Model Volatility

LLMs produce inconsistent results even on the same task: The paper demonstrates that Large Language Models show "substantial variability in sentence-level sentiment classification results," meaning the same news article could receive different sentiment scores on different runs—a critical problem when these outputs inform real financial decisions.

Small inconsistencies compound into major financial consequences: The researchers found that volatility in sentiment classification doesn't just create minor variations; it "cascades downstream, leading to more significant variations in portfolio construction and return," illustrating how unreliability at the analysis stage can translate into meaningfully different investment outcomes.

Current solutions involve difficult trade-offs: The paper identifies two potential fixes—lowering the temperature parameter to reduce randomness, or running multiple analyses and averaging results—but both have significant downsides: the first "stifles creativity" in the model's outputs, while the second requires "notable computational investment."

Financial applications demand higher reliability standards: Unlike many AI applications where occasional errors are tolerable, using LLMs for investment decisions based on news sentiment analysis requires much greater consistency, since financial choices have direct monetary consequences for investors.

Practitioners need better frameworks for managing LLM uncertainty: The authors emphasize that organizations integrating LLMs into financial decision-making need practical strategies for "adeptly navigating uncertainty," suggesting that current best practices are insufficient for high-stakes applications.