Hallucination-minimized Data-to-answer Framework for Financial Decision-makers
This addresses the problem of unreliable AI responses for financial decision-makers, though it appears incremental as it builds on existing Langchain and prompting techniques.
The authors tackled the challenge of minimizing hallucinations in LLM-based question-answering systems for financial decision-making by developing a Langchain-based framework that transforms data tables into hierarchical textual chunks and uses multi-metric scoring, achieving over 90% confidence scores for various query types.
Large Language Models (LLMs) have been applied to build several automation and personalized question-answering prototypes so far. However, scaling such prototypes to robust products with minimized hallucinations or fake responses still remains an open challenge, especially in niche data-table heavy domains such as financial decision making. In this work, we present a novel Langchain-based framework that transforms data tables into hierarchical textual data chunks to enable a wide variety of actionable question answering. First, the user-queries are classified by intention followed by automated retrieval of the most relevant data chunks to generate customized LLM prompts per query. Next, the custom prompts and their responses undergo multi-metric scoring to assess for hallucinations and response confidence. The proposed system is optimized with user-query intention classification, advanced prompting, data scaling capabilities and it achieves over 90% confidence scores for a variety of user-queries responses ranging from {What, Where, Why, How, predict, trend, anomalies, exceptions} that are crucial for financial decision making applications. The proposed data to answers framework can be extended to other analytical domains such as sales and payroll to ensure optimal hallucination control guardrails.