Discussion Articles

Articles that discuss the use of LLMs in science.

TitleType of ResourceDescription of ResourceLink to ResourceOpen ScienceUse of LLMResearch Discipline(s)
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. 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. 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). 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. Open Source Biology, Data Science, Other
“Conversing” with Qualitative Data: Enhancing Qualitative Research through Large Language Models (LLMs) Discussion Article In this paper, I explore the transformative potential of Large Language Models (LLMs) such as ChatGPT in the realm of qualitative research, particularly in the social sciences. These generative AI models, trained on extensive textual data, have the unique ability to "understand," generate, and manipulate human-like text, offering unprecedented opportunities for data analysis and interpretation. I argue that LLMs, with this capacity, can significantly enhance the depth and efficiency of qualitative analysis. They can quickly identify patterns, themes, and sentiments in the data, providing a level of nuance that can be challenging to achieve with manual coding. Furthermore, their ability to generate human-like text can be used to simulate social interactions, create engaging presentations of research findings, and even "converse" with the data in a natural and flexible way. Indeed a central contribution of this paper lies in exploring this novel concept of "asking questions of" or "conversing with" text-based data, which opens up new avenues for qualitative research and analysis. This interactive capability of LLMs provides a transformative approach to topic coding and content analysis, allowing researchers to pose complex, nuanced questions to their data and receive responses in natural language. Ethical considerations and limitations are also discussed. Preprint Data Analysis Other
AI and the transformation of social science research Discussion Article Advances in artificial intelligence (AI), particularly large language models (LLMs), are substantially affecting social science research. These transformer-based machine-learning models pretrained on vast amounts of text data are increasingly capable of simulating human-like responses and behaviors (1, 2), offering opportunities to test theories and hypotheses about human behavior at great scale and speed. This presents urgent challenges: How can social science research practices be adapted, even reinvented, to harness the power of foundational AI? And how can this be done while ensuring transparent and replicable research? Other
AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap Discussion Article The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications are developed and deployed responsibly. However, a central pillar of responsible AI—transparency—is largely missing from the current discourse around LLMs. It is paramount to pursue new approaches to provide transparency for LLMs, and years of research at the intersection of AI and human-computer interaction (HCI) highlight that we must do so with a human-centered perspective: Transparency is fundamentally about supporting appropriate human understanding, and this understanding is sought by different stakeholders with different goals in different contexts. In this new era of LLMs, we must develop and design approaches to transparency by considering the needs of stakeholders in the emerging LLM ecosystem, the novel types of LLM-infused applications being built, and the new usage patterns and challenges around LLMs, all while building on lessons learned about how people process, interact with, and make use of information. We reflect on the unique challenges that arise in providing transparency for LLMs, along with lessons learned from HCI and responsible AI research that has taken a human-centered perspective on AI transparency. We then lay out four common approaches that the community has taken to achieve transparency—model reporting, publishing evaluation results, providing explanations, and communicating uncertainty—and call out open questions around how these approaches may or may not be applied to LLMs. We hope this provides a starting point for discussion and a useful roadmap for future research. Preprint Other
Science in the Era of ChatGPT, Large Language Models and AI: Challenges for Research Ethics Review and How to Respond Discussion Article Large language models of artificial intelligence (AI) such as ChatGPT find remarkable but controversial applicability in science and research. This paper reviews epistemological challenges, ethical and integrity risks in science conduct. This is with the aim to lay new timely foundations for a high-quality research ethics review in the era of AI. The role of AI language models as a research instrument and subject is scrutinized along with ethical implications for scientists, participants and reviewers. Ten recommendations shape a response for a more responsible research conduct with AI language models. Preprint Other
Towards Human-AI Collaborative Urban Science Research Enabled by Pre-trained Large Language Models Discussion Article Pre-trained large language models (PLMs) have the potential to support urban science research through content creation, information extraction, assisted programming, text classification, and other technical advances. In this research, we explored the opportunities, challenges, and prospects of PLMs in urban science research. Specifically, we discussed potential applications of PLMs to urban institution, urban space, urban information, and citizen behaviors research through seven examples using ChatGPT. We also examined the challenges of PLMs in urban science research from both technical and social perspectives. The prospects of the application of PLMs in urban science research were then proposed. We found that PLMs can effectively aid in understanding complex concepts in urban science, facilitate urban spatial form identification, assist in disaster monitoring, and sense public sentiment. At the same time, however, the applications of PLMs in urban science research face evident threats, such as technical limitations, security, privacy, and social bias. The development of fundamental models based on domain knowledge and human-AI collaboration may help improve PLMs to support urban science research in future. Preprint Other Geography, Sociology, Urban Planning, Other
Guidance for researchers and peer‑reviewers on the ethical use of Large Language Models (LLMs) in scientific research workflows Discussion Article For researchers interested in exploring the exciting applications of Large Language Models (LLMs) in their scientific investigations, there is currently limited guidance and few norms for them to consult. Similarly, those providing peer-reviews on research articles where LLMs were used are without conventions or standards to apply or guidelines to follow. This situation is understandable given the rapid and recent development of LLMs that are capable of valuable contributions to research workflows (such as OpenAI’s ChatGPT). Nevertheless, now is the time to begin the development of norms, conventions, and standards that can be applied by researchers and peer-reviewers. By applying the principles of Artificial Intelligence (AI) ethics, we can better ensure that the use of LLMs in scientific research aligns with ethical principles and best practices. This editorial hopes to inspire further dialogue and research in this crucial area of scientific investigation