New framework ShiJianBench evaluates LLM investment advisors on long-term impact

Researchers have introduced ShiJianBench, a new offline framework designed to evaluate conversational investment advisors. This framework focuses on the long-term impact of advisor dialogue on investor decision-making, moving beyond traditional assessments of response quality or immediate outcomes. ShiJianBench utilizes a multi-agent investor simulator that models evolving states, motivations, memory, and dialogue-grounded updates, calibrated against data from thousands of real users. Experiments conducted on Chinese market data from 2021 to 2026 revealed that certain large language model (LLM) advisors demonstrated superior personalized content and competitive investor outcomes over extended periods, highlighting the importance of trajectory-aware evaluation. AI

IMPACT This framework could lead to more robust evaluations of AI agents in financial advisory roles, improving their effectiveness and safety.

RANK_REASON The cluster contains an academic paper detailing a new evaluation framework for LLM-based conversational agents. [lever_c_demoted from research: ic=1 ai=1.0]

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