Large language models like GPT-3 aren’t good enough for pharma and finance

natural language processing (NLP) is one of the most exciting subsets of machine learning. This allows us to talk to computers as if we were humans and vice versa. Siri, Google Translate, and the handy chatbots on your bank’s website all rely on this kind of AI, but not all NLP systems are created equal.

In today’s AI landscape, smaller, more targeted models trained on data that matters are often better suited for business initiatives. However, there are large-scale NLP systems with incredible communication abilities.calledlarge scale language model‘ (LLM), these can answer plain language queries and generate novel texts. Unfortunately, most of them are novelties and not suitable for the specialized work that most professional organizations require for their AI systems.

Open AI GPT-3One of the most popular LLMs is a feat of engineering. However, they also tend to output text that is subjective, inaccurate, or meaningless. This makes these huge and popular models unsuitable for industries where accuracy is critical.

favorable outlook

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There are no surefire bets in the STEM world, but the outlook for NLP technology in Europe is bright and sunny for the foreseeable future.The global market for NLP is currently estimated to be around $13.5 billion, but experts say the market is growing in Europe alone. over $21 billion By 2030.

This presents a wide open market for new startups to form, along with established industry players such as: dirty When Arria NLGThe former Dataiku was initially founded in Paris but has performed very well in the global fundraising stage and now has offices around the world. The latter company, Arria NLG, is essentially a spin-out of the University of Aberdeen and has expanded well beyond its Scottish origins. Both companies are building on their natural language processing solutions to great success by focusing on data-centric solutions that produce verifiable and accurate results for enterprise, pharmaceutical and government services.

One of the reasons these particular outlets have been so successful is that it is very difficult to train and build trustworthy AI models. For example, LLMs trained on large datasets tend to output “fake news” in the form of random statements. This is useful if you want to write ideas and inspiration, but totally unacceptable if accuracy and factual output are important.

I spoke with the CEO of one such company, Emanuel Warkner. IsopHis Paris-based company is an AI startup that specializes in using NLP for natural language generation (NLG) in standardized industries such as pharmaceuticals and finance. According to him, there is no room for error when it comes to building AI for these domains. “It has to be perfect,” he told TNW.

Yseop CEO Emmanuel Walckenaer