PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval

• URL: http://arxiv.org/abs/2511.14130v1
• Date: Tue, 18 Nov 2025 04:30:52 GMT
• Title: PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval
• Authors: Chun Chet Ng, Jia Yu Lim, Wei Zeng Low,
• Abstract summary: PRISM is a training-free framework that integrates system prompting, in-context learning, and a lightweight multi-agent system.<n>Our best configuration achieves an NDCG@5 of 0.71818 on the restricted validation split.<n>Its modular, inference-only design makes it practical for real-world use cases.
• Score: 0.3143649069042093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid progress of large language models (LLMs), financial information retrieval has become a critical industrial application. Extracting task-relevant information from lengthy financial filings is essential for both operational and analytical decision-making. The FinAgentBench dataset formalizes this problem through two tasks: document ranking and chunk ranking. We present PRISM, a training-free framework that integrates refined system prompting, in-context learning (ICL), and a lightweight multi-agent system. Each component is examined extensively to reveal their synergies: prompt engineering provides precise task instructions, ICL supplies semantically relevant few-shot examples, and the multi-agent system models coordinated scoring behaviour. Our best configuration achieves an NDCG@5 of 0.71818 on the restricted validation split. We further demonstrate that PRISM is feasible and robust for production-scale financial retrieval. Its modular, inference-only design makes it practical for real-world use cases. The source code is released at https://bit.ly/prism-ailens.

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