Articles that discuss the use of LLMs in science.
| Title | Type of Resource | Description of Resource | Link to Resource | Open Science | Use of LLM | Research Discipline(s) |
|---|---|---|---|---|---|---|
| Could AI change the scientific publishing market once and for all? | Discussion Article | Artificial-intelligence tools in research like ChatGPT are playing an increasingly transformative role in revolutionizing scientific publishing and re-shaping its economic background. They can help academics to tackle such issues as limited space in academic journals, accessibility of knowledge, delayed dissemination, or the exponential growth of academic output. Moreover, AI tools could potentially change scientific communication and academic publishing market as we know them. They can help to promote Open Access (OA) in the form of preprints, dethrone the entrenched journals and publishers, as well as introduce novel approaches to the assessment of research output. It is also imperative that they should do just that, once and for all. | Human contribution in AI-powered research | Preprint | Science Communication | Other |
| Generative AI for Economic Research: Use Cases and Implications for Economists | Discussion Article | Generative AI, in particular large language models (LLMs) such as ChatGPT, has the potential to revolutionize research. I describe dozens of use cases along six domains in which LLMs are starting to become useful as both research assistants and tutors: ideation and feedback, writing, background research, data analysis, coding, and mathematical derivations. I provide general instructions and demonstrate specic examples of how to take advantage of each of these, classifying the LLM capabilities from experimental to highly useful. I argue that economists can reap signicant productivity gains by taking advantage of generative AI to automate micro tasks. Moreover, these gains will grow as the performance of AI systems across all of these domains will continue to improve. I also speculate on the longer-term implications of AI-powered cognitive automation for economic research. The online resources associated with this paper oer instructions for how to get started and will provide regular updates on the latest capabilities of generative AI that are useful for economists. | LLM in Econ | Open Source | Other | Economics |
| Can AI language models replace human participants? | Discussion Article | Recent work suggests that language models such as GPT can make human-like judgments across a number of domains. We explore whether and when language models might replace human participants in psychological science. We review nascent research, provide a theoretical model, and outline caveats of using AI as a participant. | Replace Human | Data Collection | Psychology | |
| GUINEA PIGBOTS Doing research with human subjects is costly and cumbersome. Can AI chatbots replace them? | Discussion Article | For Kurt Gray, a social psychologist at the University of North Carolina at Chapel Hill, conducting experiments comes with certain chores. Before embarking on any study, his lab must get ethical approval from an institutional review board, which can take weeks or months. Then his team has to recruit online participants—easier than bringing people into the lab, but Gray says the online subjects are often distracted or lazy. Then the researchers spend hours cleaning the data. But earlier this year, Gray accidentally saw an alternative way to do things. He was working with computer scientists at the Allen Institute for Artificial Intelligence to see whether they could develop an AI system that made moral judgments like humans. But first they figured they’d see if a system from the startup OpenAI could already do the job. The team asked GPT-3.5, which produces eerily humanlike text, to judge the ethics of 464 scenarios, previously appraised by human subjects, on a scale from –4 (unethical) to 4 (ethical)—scenarios such as selling your house to fund a program for the needy or having an affair with your best friend’s spouse. The system’s answers, it turned out, were nearly identical to human responses, with a correlation coefficient of 0.95... | ChatBot Subjects | Open Source | Data Collection | Psychology |
| Exploring the Frontiers of LLMs in Psychological [research] Applications: A Comprehensive Review | Discussion Article | This paper explores the frontiers of large language models (LLMs) in psychology applications. Psychology has undergone several theoretical changes, and the current use of Artificial Intelligence (AI) and Machine Learning, particularly LLMs, promises to open up new research directions. We provide a detailed exploration of how LLMs like ChatGPT are transforming psychological research. It discusses the impact of LLMs across various branches of psychology, including cognitive and behavioral, clinical and counseling, educational and developmental, and social and cultural psychology, highlighting their potential to simulate aspects of human cognition and behavior. The paper delves into the capabilities of these models to emulate human-like text generation, offering innovative tools for literature review, hypothesis generation, experimental design, experimental subjects, data analysis, academic writing, and peer review in psychology. While LLMs are essential in advancing research methodologies in psychology, the paper also cautions about their technical and ethical challenges. There are issues like data privacy, the ethical implications of using LLMs in psychological research, and the need for a deeper understanding of these models' limitations. Researchers should responsibly use LLMs in psychological studies, adhering to ethical standards and considering the potential consequences of deploying these technologies in sensitive areas. Overall, the article provides a comprehensive overview of the current state of LLMs in psychology, exploring potential benefits and challenges. It serves as a call to action for researchers to leverage LLLs' advantages responsibly while addressing associated risks. | LLM in Psychological Research | Preprint | Other | Psychology |
| Control Risk for Potential Misuse of Artificial Intelligence in Science | Discussion Article | The expanding application of Artificial Intelligence (AI) in scientific fields presents unprecedented opportunities for discovery and innovation. However, this growth is not without risks. AI models in science, if misused, can amplify risks like creation of harmful substances, or circumvention of established regulations. In this study, we aim to raise awareness of the dangers of AI misuse in science, and call for responsible AI development and use in this domain. We first itemize the risks posed by AI in scientific contexts, then demonstrate the risks by highlighting real-world examples of misuse in chemical science. These instances underscore the need for effective risk management strategies. In response, we propose a system called SciGuard to control misuse risks for AI models in science. We also propose a red-teaming benchmark SciMT-Safety to assess the safety of different systems. Our proposed SciGuard shows the least harmful impact in the assessment without compromising performance in benign tests. Finally, we highlight the need for a multidisciplinary and collaborative effort to ensure the safe and ethical use of AI models in science. We hope that our study can spark productive discussions on using AI ethically in science among researchers, practitioners, policymakers, and the public, to maximize benefits and minimize the risks of misuse. | Risks AI in Sciences | Preprint | Other | Other |
| Generation Next: Experimentation with AI | Discussion Article | We investigate the potential for Large Language Models (LLMs) to enhance scientific practice within experimentation by identifying key areas, directions, and implications. First, we discuss how these models can improve experimental design, including improving the elicitation wording, coding experiments, and producing documentation. Second, we discuss the implementation of experiments using LLMs, focusing on enhancing causal inference by creating consistent experiences, improving comprehension of instructions, and monitoring participant engagement in real time. Third, we highlight how LLMs can help analyze experimental data, including pre-processing, data cleaning, and other analytical tasks while helping reviewers and replicators investigate studies. Each of these tasks improves the probability of reporting accurate findings. | Generation Next | Open Source | Other | Economics |
| The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence | Discussion Article | Recent advances in machine learning and AI, including Generative AI and LLMs, are disrupting technological innovation, product development, and society as a whole. AI's contribution to technology can come from multiple approaches that require access to large training data sets and clear performance evaluation criteria, ranging from pattern recognition and classification to generative models. Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Generative AI, in general, and Large Language Models in particular, may represent an opportunity to augment and accelerate the scientific discovery of fundamental deep science with quantitative models. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery, including self-driven hypothesis generation and open-ended autonomous exploration of the hypothesis space. Integrating AI-driven automation into the practice of science would mitigate current problems, including the replication of findings, systematic production of data, and ultimately democratisation of the scientific process. Realising these possibilities requires a vision for augmented AI coupled with a diversity of AI approaches able to deal with fundamental aspects of causality analysis and model discovery while enabling unbiased search across the space of putative explanations. These advances hold the promise to unleash AI's potential for searching and discovering the fundamental structure of our world beyond what human scientists have been able to achieve. Such a vision would push the boundaries of new fundamental science rather than automatize current workflows and instead open doors for technological innovation to tackle some of the greatest challenges facing humanity today. | Fundamental Science | Preprint | Other | Other |
| Recognizing and Utilizing Novel Research Opportunities with Artificial Intelligence | Discussion Article | As we are witnessing a fundamental transformation of organizations, societies, and economies through the rapid growth of data and development of digital technology (George, Osinga, Lavie, & Scott, 2016), artificial intelligence (AI) has the potential to transform the management field. With the power to automatize, provide predictions of outcomes, and discover patterns in massive amounts of data (Iansiti & Lakhani, 2020), AI changes many aspects of contemporary organizing, including decision-making, problem-solving, and other processes (Bailey, Faraj, Hinds, Leonardi, & von Krogh, 2022). AI also enables firms with capabilities for offering new products and services, developing new business models, and connecting stakeholders. In line with these developments, AI is not only an interesting phenomenon to study in and around organizations (e.g., Krakowski, Luger, & Raisch, 2022; Tang et al., 2022; Tong, Jia, Luo, & Fang, 2021), but also offers management scholars a wealth of research opportunities in enlarging their methodological toolbox to leverage vast amounts and various types of data (e.g., Choudhury, Allen, & Endres, 2020; Vanneste & Gulati, 2022). In our quest to push scientific boundaries, we encourage authors to explore these opportunities within Academy of Management Journal (AMJ). | AMJ Article | Open Source | Other | Business |
| From knowledge discovery to knowledge creation: How can literature-based discovery accelerate progress in science? | Discussion Article | This essay gives an overview and describes prospects for generating new scientific knowledge from disparate datasets, as viewed by four active practitioners from around the globe (Illinois, Arizona, Slovenia and Australia). Although artificial intelligence (AI) and machine learning (ML) are central techniques employed in the field, the key concepts in this essay are undiscovered public knowledge (UPK) and literature-based discovery (LBD). These comprise a variety of situations, including some not yet tackled via ML. | Knowledge Discovery | Open Source | Biology, Data Science, Other |