Learn how Altilia is leveraging GPT and Large Language Models to enhance its IDP platform

How to facilitate the adoption of Large Language Models in pharma and finance

By altilia on November 24, 2022

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.

By altilia on November 24, 2022

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How the technology behind Chat GPT can work for your organization

The explosion of interest and publicity in Artificial Intelligence in recent months has come from the advent of Large Language Models, specifically OpenAI’s ChatGPT, which set the record for the fastest-growing user base in January. Suddenly it seems like everyone is fascinated by the coming surge of AI with new applications, creating excitement and fear for the future. When Google’s so-called “Godfather of AI” Dr Geoffrey Hinton warned about “quite scary” dangers, it made headlines around the world. Behind the hype So, it is important to understand what is behind the hype and see how it works and what your organization can use to build future value. This blog is split into two: first we learn about Natural Language Processing, the branch of computer science concerned with giving machines the ability to understand text and spoken words in much the same way humans can. And then we will go deeper on Large Language Models (LLMs), which is what ChatGPT and others like Google’s Bard are using. NLP combines computational linguistics with statistical, machine learning, and deep learning models to enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes. There are two sub-fields of NLP: Natural Language Understanding (NLU) uses syntactic and semantic analysis of text and speech to determine the meaning of a sentence, similarly to how humans do it naturally. Altilia uses Large Language Models for this. Natural Language Generation (NLG) enables computers to write a human language text response based on data input. ChatGPT uses LLMs for NLG. Large Language Models (LLMs) LLMs are a relatively new approach where massive amounts of text are fed into the AI algorithm using unsupervised learning to create a “foundation” model, which can use transfer learning to continually learn new tasks. The key is using huge volumes of data. The training data for ChatGPT comes from a diverse set of text sources, including billions of web pages from the internet, a huge number of books from different genres, articles from news websites, magazines and academic journals and social media platforms such as Twitter, Reddit and Facebook to learn about informal language and the nuances of social interactions. The model is then able to predict the next word in a sentence and generate coherent text in a wide range of language tasks. Altilia does exactly the same, but uses this capability to provide enterprise tools for specific business use cases. Technology breakthrough Overall, NLP is the core technology to understand the content of documents. LLMs are a breakthrough in the field as they allow a shift from where an NLP model had to be trained in silos for a specific task to one where LLMs can leverage accumulated knowledge with transfer learning. In practice, this means we can apply a pre-trained LLM and fine-tune it with a relatively small dataset to allow the model to learn new customer-specific or use-case specific tasks. We are then able to scale up more effectively, it can be applied more easily for different use cases, leading to a higher ROI. For more information on how Altilia Intelligent Automation can support your organization to see radical improvements in accuracy and efficiency, schedule a demo here.

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