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The Impact of GPT and Generative AI Models on People Analytics (Interview with Andrew Marritt)

By | Andrew Marritt | David Green |

Since ChatGPT was launched in November 2022, there has been a surge of interest and some concern about the potential impact of GPT and Generative AI models on business as well as on HR and workers. One recent report by Goldman Sachs suggests that AI could replace the equivalent of 300 million full-time jobs, as well as a quarter of work tasks in the US and Europe. However, the same report also predicts that, similar to previous industrial revolutions, there will be new jobs and a productivity boom.

What will the impact be on HR and specifically on people analytics? To find out more, I was delighted to recently catch up with Andrew Marritt – someone who has their finger firmly on the pulse when it comes to large language models and text analytics. Andrew is the CEO of Organizational View, a specialist firm that uses quantitative research methods augmented by AI to analyse employee feedback and workforce data. Andrew has been working in the people analytics field since 2008, publishes the brilliant Empirical HR newsletter, and is also an instructor on a course on the myHRfuture Academy: Applying Text Analytics to HR Data.

I started the discussion by asking Andrew to provide more information about GPT models:

What are GPT models, and where will they (and similar models) be used in People Analytics?

Today, GPT models are best known via ChatGPT, a model introduced by OpenAI in November ’22 but already surpassed by their GPT-4 models. They belong to a class of models which are often called ‘foundational models’ or ‘large language models’. There are a rapidly growing number of these generative models available to the analyst. As well as the OpenAI suite some other interesting providers include Cohere, Antrophic’s Claude models, and Google’s PaLM models. (For simplification, I’m going to discuss the OpenAI models here).

The current LLMs are mostly created by a process called self-supervised learning. The models are trained on very large volumes of data where words are masked out, and the model trained to predict the hidden word. In this way, they’re probabilistic models – they don’t understand the words, but in a large context they can often accurately predict the next one or ones. At the moment it seems like one way of making these more performant is by increasing the number of tokens (words). We’re therefore seeing a race to produce larger and larger models.

The recent LLMs also use a secondary process called human feedback reinforcement learning. They’re using a team of reviewers to assess machine-generated text to assess which is the most ‘human-like’. This process ensures that the resulting generated text appears human-like.

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