The Analyst in the Prompt: Role, Retrieval, and Memory Biases in LLM Financial Analysis

Abstract:Large Language Models (LLMs) increasingly use user context such as memory, profiles, and role prompts to personalize their responses. This personalization can affect evidence-based judgment: the same evidence may lead to different conclusions under different user contexts. Finance provides a high-stakes setting to study this problem because decisions often depend on interpreting long and complex documents. We test this using 3,575 SEC filings across twelve LLMs. We compare persona-conditioned retrieval, neutral retrieval, and memory-framed context to separate the effect of evidence selection from the effect of interpretation. We find that most user-context spillover comes from how models interpret the same evidence under different roles, rather than from retrieving different evidence. We then test two simple mitigation strategies: expressing the same investor mindset as a user profile instead of an assistant role, and separating evidence-based and personalized outputs. Both reduce spillover, but neither removes it completely, and their effectiveness varies substantially across models.

Computation and Language (cs.CL); Computational Engineering, Finance, and Science (cs.CE); Portfolio Management (q-fin.PM)

arXiv:2609.03218 [cs.CL]

(or arXiv:2609.03218v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.03218 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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