# Friend or Foe? Exploring the Implications of Large Language Models on the Science System

arXiv: https://arxiv.org/abs/2306.09928 · HTML: https://mlsanity.com/paper/2306.09928/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 49 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023)
- [Regulating ChatGPT and other Large Generative AI Models](https://mlsanity.com/paper/2302.02337/) (2023)
- [ChatGPT and a New Academic Reality: Artificial Intelligence-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing](https://mlsanity.com/paper/2303.13367/) (2023)
- [ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review](https://mlsanity.com/paper/2305.03123/) (2023)
- [Academic Writing with GPT-3.5: Reflections on Practices, Efficacy and Transparency](https://mlsanity.com/paper/2304.11079/) (2023)
- [ChatGPT and Works Scholarly: Best Practices and Legal Pitfalls in Writing with AI](https://mlsanity.com/paper/2305.03722/) (2023)

## Built on by (most cited first, 16 of 16)

- [From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?](https://mlsanity.com/paper/2308.01990/) (2023)
- [LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions](https://mlsanity.com/paper/2411.05025/) (2024)
- [Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review](https://mlsanity.com/paper/2401.01519/) (2024)
- [Near to Mid-term Risks and Opportunities of Open-Source Generative AI](https://mlsanity.com/paper/2404.17047/) (2024)
- [Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles](https://mlsanity.com/paper/2406.10773/) (2024)
- [How should the advent of large language models affect the practice of science?](https://mlsanity.com/paper/2312.03759/) (2023)
- [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025)
- [Beyond principlism: Practical strategies for ethical AI use in research practices](https://mlsanity.com/paper/2401.15284/) (2024)
- [Delving into the Utilisation of ChatGPT in Scientific Publications in Astronomy](https://mlsanity.com/paper/2406.17324/) (2024)
- [OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models](https://mlsanity.com/paper/2505.21537/) (2025)
- [Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models](https://mlsanity.com/paper/2508.17184/) (2025)
- [What is the Role of Large Language Models in the Evolution of Astronomy Research?](https://mlsanity.com/paper/2409.20252/) (2024)
- [Potential Societal Biases of ChatGPT in Higher Education: A Scoping Review](https://mlsanity.com/paper/2311.14381/) (2023)
- [Deconstructing Jazz Piano Style Using Machine Learning](https://mlsanity.com/paper/2504.05009/) (2025)
- [Will Scaling Improve Social Simulation with LLMs?](https://mlsanity.com/paper/2607.02464/) (2026)
- [LLM assisted writing deserves empirical evaluation](https://mlsanity.com/paper/2608.22124/) (2026)

## Abstract (arXiv)

The advent of ChatGPT by OpenAI has prompted extensive discourse on its potential implications for science and higher education. While the impact on education has been a primary focus, there is limited empirical research on the effects of large language models (LLMs) and LLM-based chatbots on science and scientific practice. To investigate this further, we conducted a Delphi study involving 72 experts specialising in research and AI. The study focused on applications and limitations of LLMs, their effects on the science system, ethical and legal considerations, and the required competencies for their effective use. Our findings highlight the transformative potential of LLMs in science, particularly in administrative, creative, and analytical tasks. However, risks related to bias, misinformation, and quality assurance need to be addressed through proactive regulation and science education. This research contributes to informed discussions on the impact of generative AI in science and helps identify areas for future action.