Articles that discuss the use of LLMs in science.
| Title | Type of Resource | Link to Resource | Date Recorded | Open Science | Use of LLM | Research Discipline(s) | Description of Resource |
|---|---|---|---|---|---|---|---|
| ChatGPT is a Remarkable Tool—For Experts | Discussion Article | Experts | September 23, 2024 | Open Source | Other | Other | This paper investigates the capabilities of ChatGPT as an automated assistant in diverse domains, including scientific writing, mathematics, education, programming, and healthcare. We explore the potential of ChatGPT to enhance productivity, streamline problem-solving processes, and improve writing style. Furthermore, we highlight the potential risks associated with excessive reliance on ChatGPT in these fields. These limitations encompass factors like incorrect and fictitious responses, inaccuracies in code, limited logical reasoning abilities, overconfidence, and critical ethical concerns of copyright and privacy violation. We outline areas and objectives where ChatGPT proves beneficial, applications where it should be used judiciously, and scenarios where its reliability may be limited. In light of observed limitations, and given that the tool's fundamental errors may pose a special challenge for non-experts, ChatGPT should be used with a strategic methodology. By drawing from comprehensive experimental studies, we offer methods and flowcharts for effectively using ChatGPT. Our recommendations emphasize iterative interaction with ChatGPT and independent verification of its outputs. Considering the importance of utilizing ChatGPT judiciously and with expertise, we recommend its usage for experts who are well-versed in the respective [research] domains. [https://direct.mit.edu/view-large/figure/4705485/dint_a_00235.figure.12.jpg] |
| The advantages and limitations of using ChatGPT to enhance technological research | Discussion Article | Tech Resesarch | September 23, 2024 | Open Source | Research Design, Data Analysis, Describing Results, Science Communication | Other | In 2022, OpenAI made a groundbreaking entrance with the release of ChatGPT, a new online chatbot that allows users to interact with the GPT-3.5 language model. Users can ask questions and converse with ChatGPT by typing into a text field similar to direct messaging software. Then, ChatGPT will generate a response. Users can then either respond to ChatGPT, regenerate the previous response, or “like” the response and give feedback. OpenAI improved the program on March 14th, 2023, with the release of GPT 4, which promised better reasoning ability. Both these iterations of ChatGPT have attracted significant attention from researchers due to the software's remarkably enhanced capabilities compared to earlier versions. Due to its inherent value as a research tool, ChatGPT will likely become a permanent fixture, so a thorough evaluation of ethical and professional boundaries is crucial. In this opinion paper, we explore ChatGPT 4.0 by addressing: a) its capabilities, b) its limitations and weaknesses, and c) strategies for fact-checking its output to ensure high-quality responses. Subsequently, the authors delve into the diverse implications of this software and discuss how it can be optimally employed to advance research in technology and various other domains. |
| How will generative AI disrupt data science in drug discovery? | Discussion Article | Drug discovery | September 23, 2024 | Open Source | Other | Biology, Other | In the short few months since the release of ChatGPT1,2, the potential for large language models (LLMs) and generative artificial intelligence (AI) to disrupt fields as diverse as art, marketing, journalism, copywriting, law and software engineering is already being realized. These technologies use deep learning models trained on enormous amounts of data to generate new texts or images. While trained only to capture statistical regularities in the training data, their ability, once trained, to imitate human language in a convincing way; to generate realistic images, sounds or software; or to solve tasks apparently involving higher cognitive functions such as reasoning has caught the world by surprise. As such, they are also poised to disrupt in many ways how scientists and engineers understand biology and discover and develop new treatments. |
| ‘ChatGPT et al.’: The ethics of using (generative) artificial intelligence in research and science | Discussion Article | Ethics | September 23, 2024 | Open Source | Other | Computer Science, Data Science, Other | As journal editors, the emergence of ChatGPT prompted us – and others (e.g. Hill-Yardin et al., 2023; Liebrenz et al., 2023; Lund and Wang, 2023; Teubner et al., 2023; Van Dis et al., 2023) – to ask foundational questions about using generative AI in research and science. Specifically: Is it ‘ethical’ to use generative or other AIs in conducting research or for writing academic research papers? In this editorial, we go back to first principles to reflect on the fundamental ethics to apply to using ChatGPT and AI in research and science. Next, we caution that (generative) AI is also at the ‘peak of inflated (hype) expectations’ and discuss eight in-principle issues that AI struggles with, both ethically and practically. We conclude with what this all means for the ethics of using generative AI in research and science. |
| Ten simple rules for using large language models in science | Discussion Article | 10 Rules | September 22, 2024 | Open Source | Other | Biology, Public Health, Other | Generative artificial intelligence (AI) tools, including large language models (LLMs), are expected to radically alter the way we live and work, with as many as 300 million jobs at risk [1]. Arguably the most well-known LLM currently is GPT (generative pre-trained transformer), developed by American company OpenAI [2]. Since its release in late 2022, GPT’s chatbot interface, ChatGPT, has exploded in popularity, setting a new record for the fastest growing user base in history [3]. The appeal of GPT and other LLMs stem from their ability to effectively carry out multistep tasks and provide clear, human-like responses to complicated queries and prompts (Box 1). Unsurprisingly, this capacity is catching the eye of scientists [4]. |
| Automated Social Science: Language Models as Scientist and Subjects | Discussion Article | Automated Social Science | March 11, 2024 | Open Source | Other | Other | We present an approach for automatically generating and testing, in silico, social scientific hypotheses. This automation is made possible by recent advances in large language models (LLM), but the key feature of the approach is the use of structural causal models. Structural causal models provide a language to state hypotheses, a blueprint for constructing LLM-based agents, an experimental design, and a plan for data analysis. The fitted structural causal model becomes an object available for prediction or the planning of follow-on experiments. We demonstrate the approach with several scenarios: a negotiation, a bail hearing, a job interview, and an auction. In each case, causal relationships are proposed and tested, finding evidence for some and not others. In the auction experiment, we show that the in silico simulation results closely match the predictions of auction theory, but elicited predictions of the clearing prices from an LLM are inaccurate. However, the LLM’s predictions are dramatically improved if the model can condition on the fitted structural causal model. When given a proposed structural causal model for one of the scenarios, the LLM is good at predicting the signs of estimated effects, but it cannot reliably predict the magnitudes of those effects. This suggests that social simulations give the model insight not available purely through direct elicitation. In short, the LLM knows more than it can (immediately) tell. |
| Start Generating: Harnessing Generative Artificial Intelligence for Sociological Research | Discussion Article | Sociological | September 22, 2024 | Open Source | Other | Sociology | How can generative artificial intelligence (GAI) be used for sociological research? The author explores applications to the study of text and images across multiple domains, including computational, qualitative, and experimental research. Drawing upon recent research and stylized experiments with DALL·E and GPT-4, the author illustrates the potential applications of text-to-text, image-to-text, and text-to-image models for sociological research. Across these areas, GAI can make advanced computational methods more efficient, flexible, and accessible. The author also emphasizes several challenges raised by these technologies, including interpretability, transparency, reliability, reproducibility, ethics, and privacy, as well as the implications of bias and bias mitigation efforts and the trade-offs between proprietary models and open-source alternatives. When used with care, these technologies can help advance many different areas of sociological methodology, complementing and enhancing our existing toolkits. |
| “ChatGPT Assists Me in My Reference List:” Exploring the Chatbot’s Potential as Citation Formatting Tool | Discussion Article | Citations | September 22, 2024 | Science Communication, Other | Other | This inquiry unveiled the potential of ChatGPT as a viable alternative to traditional citation generator. Findings showed the substantial potential and reliability of the chatbot as a citation formatting tool. Notably, the study revealed ChatGPT’s remarkable accuracy in configuring reference citations for journal articles and books across a range of styles, including APA 7, MLA 9, IEEE, and Harvard. Furthermore, the tool demonstrated proficiency in organizing in-text citations for multiple references. Despite the commendable performance of ChatGPT, manual editing remains essential for the final verification of the references to ensure the utmost accuracy and credibility of sourced materials. | |
| Challenges and Applications of Large Language Models | Discussion Article | Challenges and Applications of L | August 12, 2024 | Preprint | Other | Other | Large Language Models (LLMs) went from non-existent to ubiquitous in the machine learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify the remaining challenges and already fruitful application areas. In this paper, we aim to establish a systematic set of open problems and application successes so that ML researchers can comprehend the field's current state more quickly and become productive. [with discussion of challenges related to scientific applications] |
| We Have No Satisfactory Social Epistemology of AI-Based Science | Discussion Article | No Satisfactory Social Epistemology | June 9, 2024 | Open Source | Other | Philosophy | In the social epistemology of scientific knowledge, it is largely accepted that relationships of trust, not just reliance, are necessary in contemporary collaborative science characterised by relationships of opaque epistemic dependence. Such relationships of trust are taken to be possible only between agents who can be held accountable for their actions. But today, knowledge production in many fields makes use of AI applications that are epistemically opaque in an essential manner. This creates a problem for the social epistemology of scientific knowledge, as scientists are now epistemically dependent on AI applications that are not agents, and therefore not appropriate candidates for trust. |