[2511.14130] PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval

Computer Science > Artificial Intelligence arXiv:2511.14130 (cs) [Submitted on 18 Nov 2025 (v1), last revised 6 Apr 2026 (this version, v2)] Title:PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval Authors:Chun Chet Ng, Jia Yu Lim, Wei Zeng Low View a PDF of the paper titled PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval, by Chun Chet Ng and 2 other authors View PDF HTML (experimental) 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. We present PRISM, a training-free framework that integrates refined system prompting, in-context learning (ICL), and lightweight multi-agent coordination for document and chunk ranking tasks. Our primary contribution is a systematic empirical study of when each component provides value: prompt engineering delivers consistent performance with minimal overhead, ICL enhances reasoning for complex queries when applied selectively, and multi-agent systems show potential primarily with larger models and careful architectural design. Extensive ablation studies across FinAgentBench, FiQA-2018, and FinanceBench reveal that simpler configurations often outperform complex multi-agent pipelines, providing practical guidance for practitioners. Our best configuration achieves an NDCG...