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

arXiv: https://arxiv.org/abs/2311.10961 · HTML: https://mlsanity.com/paper/2311.10961/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Automating Customer Service using LangChain: Building custom open-source GPT Chatbot for organizations](https://mlsanity.com/paper/2310.05421/) (2023)
- [Hallucination-minimized Data-to-answer Framework for Financial Decision-makers](https://mlsanity.com/paper/2311.07592/) (2023)
- [Learning to Plan with Natural Language](https://mlsanity.com/paper/2304.10464/) (2023)

## Built on by (most cited first, 7 of 7)

- [A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Models](https://mlsanity.com/paper/2405.09589/) (2024)
- [Large Language Models Hallucination: A Comprehensive Survey](https://mlsanity.com/paper/2510.06265/) (2025)
- [RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration](https://mlsanity.com/paper/2503.13514/) (2025)
- [FAITH: A Framework for Assessing Intrinsic Tabular Hallucinations in Finance](https://mlsanity.com/paper/2508.05201/) (2025)
- [Genetic Approach to Mitigate Hallucination in Generative IR](https://mlsanity.com/paper/2409.00085/) (2024)
- [Leveraging Approximate Caching for Faster Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.05530/) (2025)
- [From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini](https://mlsanity.com/paper/2510.11269/) (2025)

## Abstract (arXiv)

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 that are meant for decision makers. In this work we present the three major stages in the journey of designing hallucination-minimized LLM-based solutions that are specialized for the decision makers of the financial domain, namely: prototyping, scaling and LLM evolution using human feedback. These three stages and the novel data to answer generation modules presented in this work are necessary to ensure that the Generative AI chatbots, autonomous reports and alerts are reliable and high-quality to aid key decision-making processes.