# Empirical assessment of ChatGPT's answering capabilities in natural science and engineering

arXiv: https://arxiv.org/abs/2309.10048 · HTML: https://mlsanity.com/paper/2309.10048/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2023.

## Built on by (most cited first, 4 of 4)

- [Are large language models superhuman chemists?](https://mlsanity.com/paper/2404.01475/) (2024)
- ["I'd Like to Have an Argument, Please": Argumentative Reasoning in Large Language Models](https://mlsanity.com/paper/2309.16938/) (2023)
- [GraphRAG for Engineering Diagrams: ChatP&ID Enables LLM Interaction with P&IDs](https://mlsanity.com/paper/2603.22528/) (2026)
- [Integrating Text-to-Music Models with Language Models: Composing Long Structured Music Pieces](https://mlsanity.com/paper/2410.00344/) (2024)

## Abstract (arXiv)

ChatGPT is a powerful language model from OpenAI that is arguably able to comprehend and generate text. ChatGPT is expected to greatly impact society, research, and education. An essential step to understand ChatGPT's expected impact is to study its domain-specific answering capabilities. Here, we perform a systematic empirical assessment of its abilities to answer questions across the natural science and engineering domains. We collected 594 questions on natural science and engineering topics from 198 faculty members across five faculties at Delft University of Technology. After collecting the answers from ChatGPT, the participants assessed the quality of the answers using a systematic scheme. Our results show that the answers from ChatGPT are, on average, perceived as ''mostly correct''. Two major trends are that the rating of the ChatGPT answers significantly decreases (i) as the educational level of the question increases and (ii) as we evaluate skills beyond scientific knowledge, e.g., critical attitude.