Communications of the ACM
Thus Spake ChatGPT
Credit: Moor Studio
It has been three years since the arrival of GPT-3,2 a neural-network-based large language model (LLM) that could generate polished, stylish text with ease. What followed was an intense race among tech giants like Google, OpenAI, and Meta to come up with larger and larger models, often trained using texts appearing anywhere on the Internet. While storming the AI research communities, all these were much outside the scope of usual public discourse. And then came OpenAI's ChatGPT3—an AI chatbot running on top of an LLM. The advancement was evident, shortcomings were subtle. But the most drastic change that ChatGPT brought forth was the sensation among the non-researchers (or researchers far from language technology or artificial intelligence) and ordinary citizens. With the online interface available to everyone on the Internet, it did not take much to become the talk of the town. ChatGPT can explain quantum computing to a non-expert, write poems in Keats' style, write and debug JavaScript codes, and many more. And very soon we started wondering: Can ChatGPT replace search engines such as Google, simplified knowledge sources like Wikipedia, or community-powered Q&A platforms like StackExchange? To get an idea of its mammoth popularity, ChatGPT garnered more than 100 million users during just two months after launching; it took 2.5 years for Instagram and nine months for Tik-Tok to achieve the same amount of users.8 Beyond the shock and awe, it is important to identify the potential impact of the large-scale deployment of such AI-based chatbots on the production and consumption of scientific knowledge. Specifically, how would one envision the role of an AI chatbot being used by nonexperts as a source to quench their thirst for scientific knowledge?
Popular Majority vs. Authentic Minority
It is very much accepted in the LLM research that however proficient these models are in generating polished, confident textual responses to queries, they lack factual consistency.6 Scaling to even larger models has significantly decreased these inconsistencies from the earlier models. However, we are not just talking about blunt factual errors here; ChatGPT is smarter in that sense. When the goal is to communicate hardcore scientific facts to non-expert audiences, misconceptions can percolate with apparently synonymous phrasings—very often overlooked by even the experts. For example, consider the conversation with ChatGPTa per the following text:
User: Explain why spacetime is modeled as a Riemannian manifold.
ChatGPT: Spacetime is modeled as a Riemannian manifold because it allows for a mathematical representation of curved spacetime, as predicted by general relativity. The geometry of a Riemannian manifold can be described by a metric tensor, which encodes information about distances and angles between nearby points in the manifold. In the case of spacetime, the metric tensor is determined by the distribution of mass and energy in the universe and it determines how the objects move in response to gravity. The Riemannian manifold framework provides a powerful and flexible way to describe the curvature of space-time, and it has been extremely successful in explaining a wide range of physical phenomenon, from the motion of the planets to behavior of black holes.
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