Yes, You Still Need NLP Skills in “the Age of ChatGPT”

Large Language Models have their strengths, but for many production problems, simpler NLP techniques are faster, cheaper, and just as effective.

Large Language Models require new skills, but it’s important not to forget the old ones too, like how to prepare the text data the LLM should use. Source: Markus Winkler on Unsplash.

Back when I started a masters of Computational Linguistics, no-one I knew had even the faintest idea what Natural Language Processing (NLP) was. Not even me [1]. Fast forward four years, and now when I say I work in NLP, I only get blank looks about half of the time [2]. Thanks to masses of media hype, most people know that there are things called Large Language Models, and they can do a lot of amazing and very useful stuff with text. It’s become a lot easier for me to explain my job to people (provided I tell them “it’s basically ChatGPT”). But recently, this also gave me pause.

I’m the editor of a Data Science and AI textbook, published waaaay back in 2022 (seriously, in AI years that’s about 20). In preparation for the third edition, coming this year, I needed to update my NLP chapter. And as I sat down to read what I wrote back then about neural networks, sequence to sequence models, and this damn-fangled new technology called “Transformers,” I noticed something remarkable: it all felt so old school. All that stuff on statistical Machine Learning approaches? Quaint. And my little code…