The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applications
For developers deploying LLMs in financial applications, this work highlights a specific failure mode (sycophancy) that differs from general-domain findings, necessitating tailored safety evaluations.
This paper evaluates sycophancy in LLMs used for agentic financial tasks, finding low to modest performance drops under user rebuttals but significant failures when user preferences contradict reference answers. They introduce a new benchmark and test recovery methods like input filtering.
Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems. One failure mode that LLMs frequently display in general domain settings is that of sycophancy. That is, models prioritize agreement with expressed user beliefs over correctness, leading to decreased accuracy and trust. In this work, we focus on evaluating sycophancy that LLMs display in agentic financial tasks. Our findings are three-fold: first, we find the models show only low to modest drops in performance in the face of user rebuttals or contradictions to the reference answer, which distinguishes sycophancy that models display in financial agentic settings from findings in prior work. Second, we introduce a suite of tasks to test for sycophancy by user preference information that contradicts the reference answer and find that most models fail in the presence of such inputs. Lastly, we benchmark different modes of recovery such as input filtering with a pretrained LLM.