arXiv:2606.29793v3 Announce Type: replace
Abstract: Demand for personalized financial advice is growing, yet current LLM-based advisors often fail to provide consistent and specialized guidance.\ Sim...
By Suhwan Park, Hoyoung Lee, Zhangyang Wang, Alejandro Lopez-Lira, Young Cha, Chanyeol Choi, Jaewon Choi, Yongjae Lee
The paper investigates how user context—such as memory, profiles, and role prompts—affects large language models’ financial analysis. By testing 3,575 SEC filings across twelve LLMs, the study distinguishes between evidence selection and interpretation, finding that interpretation under different roles drives most user-context spillover. Two mitigation strategies—using a user profile instead of an assistant role and separating evidence-based from personalized outputs—reduce but do not eliminate this spillover, with effectiveness varying by model.
The study examines how user context—such as memory, profiles, and role prompts—affects Large Language Models’ (LLMs) financial analysis. Using 3,575 SEC filings and twelve LLMs, the authors distinguish between evidence selection and interpretation, finding that most context spillover arises from differing interpretations under various roles rather than from retrieving different evidence. They evaluate two mitigation strategies—expressing investor mindset as a user profile instead of an assistant role, and separating evidence-based from personalized outputs—both of which reduce but do not eliminate spillover, with effectiveness varying across models.
By Ahmed Asaad, Amr Mohamed, Yang Zhang, Omneya Abdelsalam
arXiv:2605.27864v5 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly applied in finance, yet most existing work emphasizes trading signals or financial NLP tasks centered...
By Di Zhu, Lei Nico Zheng, Zihan Chen
arXiv:2605. 27864v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied in finance, yet most existing work emphasizes trading signals or financial NLP tasks centered on prediction.
By Di Zhu, Lei Nico Zheng, Zihan Chen
arXiv:2608. 04095v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used as personalized assistants in high-stakes domains such as financial advising, yet it remains unclear whether they can maintain and update an individualized user model over long horizons.
By Ben Wang, Kang Zhou, Lifan Guo, Feng Chen, Chi Zhang