Journey of Hallucination-minimized Generative AI Solutions for Financial Decision Makers
Sohini Roychowdhury

TL;DR
This paper discusses the development of specialized LLM-based solutions for financial decision makers that minimize hallucinations, ensuring reliable and high-quality AI outputs through prototyping, scaling, and human feedback integration.
Contribution
It introduces a novel three-stage process—prototyping, scaling, and LLM evolution with human feedback—for creating hallucination-minimized AI solutions tailored for finance decision makers.
Findings
Effective reduction of hallucinations in financial LLM applications
Development of new data-to-answer modules for reliable outputs
Enhanced decision-making support through high-quality AI tools
Abstract
Generative AI has significantly reduced the entry barrier to the domain of AI owing to the ease of use and core capabilities of automation, translation, and intelligent actions in our day to day lives. Currently, Large language models (LLMs) that power such chatbots are being utilized primarily for their automation capabilities for software monitoring, report generation etc. and for specific personalized question answering capabilities, on a limited scope and scale. One major limitation of the currently evolving family of LLMs is 'hallucinations', wherein inaccurate responses are reported as factual. Hallucinations are primarily caused by biased training data, ambiguous prompts and inaccurate LLM parameters, and they majorly occur while combining mathematical facts with language-based context. Thus, monitoring and controlling for hallucinations becomes necessary when designing solutions…
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Taxonomy
TopicsTopic Modeling · Ferroelectric and Negative Capacitance Devices
