HOW NOT
TO USE
LLMS
WITH IDP
Introduction
Large Language Models (LLMs), like the one
behind ChatGPT, have revolutionized AI
capabilities, from acing exams to replacing
nutritionists. Integrations between Intelligent
Document Processing (IDP) vendors and LLMs
are announced, raising practical considerations.
Compute
Power Challenges
AI models, especially LLMs, are
computationally intensive. Externally
hosted LLMs incur significant costs,
impacting the ROI of IDP solutions
designed to reduce manual labor.
Consistency
Concerns
Inconsistent answers from LLMs can
compromise accuracy in document
processing, challenging the reliability of
IDP solutions relying on LLMs.
Token Limits Impact
Most LLMs have token limits, hindering
real-time data processing in IDP
solutions and potentially affecting
scalability.
Context
Maintenance Difficulty
Expensive computing leading to token
limits also poses challenges in
maintaining context during document
processing, risking accuracy in IDP.
The Great
Start Problem
Initial positive impressions of LLMs may
fade with time as challenges in viability,
reliability, and economy become
apparent in practical business
technology applications.
Learning Loop Setback
The ML Feedback Loop, vital for
continuous learning in IDP systems,
takes a step back with LLMs. Finetuning LLMs for each customer's data is
cumbersome, impacting the
incremental improvement cycle.
Conclusion
While LLMs represent progress, their blind use during
hype cycles can lead to challenges. Strategic
employment, as done at Infrrd, can enhance IDP
platforms and make customers' lives easier.
THANK YOU
To know more, visit:
https://www.infrrd.ai/blog/how-not-to-use-llms-with-idp