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