After the initial enthusiasm about Large Language Models (LLMs) due to their impressive results that made them SOTA approaches for some new and traditional general purposes NLP tasks (e.g. question answering, text classification, information retrieval, token sequence tagging, entity extraction, sentiment analysis, intent detection, word sense disambiguation, POS-tagging), now it’s the time of reality.
Peraphs, LLMs are contributing more than computervision to the new AI spring. LLMs are powerful tools that can create great value in industry and help organizations to streamline business processes, improve operations, save a lot of money, and improve business performances. But, as a recent neural article optimally points out, they must be carefully adopted, furthemore they need to be fine tuned to solve specific business problems in vertical domains.
For this reason in Altilia we have built a platform that helps users to fine tune LLMs and control their results by human-in-the-loop AI, augmented intelligence, and composite and adaptive AI tools.
Users in highly specialized areas like pharma, health, and finance have to face problems cited in the article. In particular, LLMs cannot be used as they are. These models are trained over general available data, hence specific data programming and fine-tuning techniques are needed to make them working in a trustworthy, explainable way on highly specific, regulated, privacy and security sensitive tasks.
Altilia Intelligent Automation platform leverages all LLMs available in huggingface and provides users with tools that allow to curate and program data set to fine-tune LLMs and apply them to solve any NLP and document processing task at the highest level of accuracy and trustworthiness needed by the users operating in highly regulated environments.
For more information read the following use cases: improving NPL data tapes management; enhancing credit scoring for lending applications; Extract ESG data from multiple sources; data extraction from notes to balance sheets.