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New PRISM framework boosts LLM financial retrieval without fine-tuning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PRISM, a novel framework designed to enhance the accuracy and cost-efficiency of large language models (LLMs) in financial information retrieval without requiring fine-tuning. PRISM integrates prompt engineering, in-context learning, and a candidate refinement module, demonstrating improved performance on benchmarks like FiQA-2018 and FinAgentBench. The framework also introduces FACET, a tool for selecting optimal configurations based on quality, cost, and latency trade-offs, and releases an expert-annotated evaluation set to ensure reproducible research. AI

IMPACT This framework offers a cost-effective method for improving LLM performance in specialized domains like finance, potentially reducing the need for expensive fine-tuning.

RANK_REASON The cluster contains a research paper detailing a new framework for LLM applications. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PRISM framework boosts LLM financial retrieval without fine-tuning

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The cluster contains a research paper detailing a new framework for LLM applications. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Chun Chet Ng, Jia Yu Lim, Zhen Hao Chu, Yixi Zhou, Wei Zeng Low ·

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

    arXiv:2511.14130v3 Announce Type: replace-cross Abstract: Deploying LLMs for financial information retrieval demands accuracy, cost efficiency, and reproducibility, yet most approaches require expensive fine-tuning. PRISM is a training-free framework that composes three deployabl…

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