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 |
|---|---|---|---|---|---|---|---|
| How should the advancement of large language models affect the practice of science? | Discussion Article | How Should | April 2, 2025 | Open Source | Other | Any Discipline | Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the advancement of large language models affect the practice of science? For this opinion piece, we have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate. Schulz et al. make the argument that working with LLMs is not fundamentally different from working with human collaborators, while Bender et al. argue that LLMs are often misused and overhyped, and that their limitations warrant a focus on more specialized, easily interpretable tools. Marelli et al. emphasize the importance of transparent attribution and responsible use of LLMs. Finally, Botvinick and Gershman advocate that humans should retain responsibility for determining the scientific roadmap. To facilitate the discussion, the four perspectives are complemented with a response from each group. By putting these different perspectives in conversation, we aim to bring attention to important considerations within the academic community regarding the adoption of LLMs and their impact on both current and future scientific practices. |
| Large language models (LLM) in computational social science: prospects, current state, and challenges | Discussion Article | CSS | April 2, 2025 | Open Source | Other | Business, Education, Economics, Political Science, Psychology, Public Health, Sociology | The advent of large language models (LLMs) has marked a new era in the transformation of computational social science (CSS). This paper dives into the role of LLMs in CSS, particularly exploring their potential to revolutionize data analysis and content generation and contribute to a broader understanding of social phenomena. We begin by discussing the applications of LLMs in various computational problems in social science including sentiment analysis, hate speech detection, stance and humor detection, misinformation detection, event understanding, and social network analysis, illustrating their capacity to generate nuanced insights into human behavior and societal trends. Furthermore, we explore the innovative use of LLMs in generating social media content. We also discuss the various ethical, technical, and legal issues these applications pose, and considerations required for responsible LLM usage. We further present the challenges associated with data bias, privacy, and the integration of these models into existing research frameworks. This paper aims to provide a solid background on the potential of LLMs in CSS, their past applications, current problems, and how they can pave the way for revolutionizing CSS. |
| Scaling Laws of Scientific Discovery with AI and Robot Scientists | Discussion Article | Scalling | March 31, 2025 | Preprint | Other | Any Discipline | The rapid evolution of scientific inquiry highlights an urgent need for groundbreaking methodologies that transcend the limitations of traditional research. Conventional approaches, bogged down by manual processes and siloed expertise, struggle to keep pace with the demands of modern discovery. We envision an autonomous generalist scientist (AGS) system-a fusion of agentic AI and embodied robotics-that redefines the research lifecycle. This system promises to autonomously navigate physical and digital realms, weaving together insights from disparate disciplines with unprecedented efficiency. By embedding advanced AI and robot technologies into every phase-from hypothesis formulation to peer-ready manuscripts-AGS could slash the time and resources needed for scientific research in diverse field. We foresee a future where scientific discovery follows new scaling laws, driven by the proliferation and sophistication of such systems. As these autonomous agents and robots adapt to extreme environments and leverage a growing reservoir of knowledge, they could spark a paradigm shift, pushing the boundaries of what's possible and ushering in an era of relentless innovation. |
| Autonomous chemical research with large language models | Discussion Article | Chemistry | March 3, 2025 | Open Source | Other | Chemistry | Transformer-based large language models are making significant strides in various fields, such as natural language processing1,2,3,4,5, biology6,7, chemistry8,9,10 and computer programming11,12. Here, we show the development and capabilities of Coscientist, an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation. Coscientist showcases its potential for accelerating research across six diverse tasks, including the successful reaction optimization of palladium-catalysed cross-couplings, while exhibiting advanced capabilities for (semi-)autonomous experimental design and execution. Our findings demonstrate the versatility, efficacy and explainability of artificial intelligence systems like Coscientist in advancing research. |
| A Primer for Evaluating Large Language Models in Social Science Research | Discussion Article | Primer | February 12, 2025 | Preprint | Other | Psychology | Autoregressive Large Language Models (LLMs) exhibit remarkable conversational and reasoning abilities, and exceptional flexibility across a wide range of tasks. Subsequently, LLMs are being increasingly used in scientific research, to analyze data, generate synthetic data, or even to write scientific papers. This trend necessitates that authors follow best practices for conducting and reporting LLM research and that journal reviewers are able to evaluate the quality of works that use LLMs. We provide authors of social scientific research with essential recommendations to ensure replicable and robust results using LLMs. Our recommendations also highlight considerations for reviewers, focusing on methodological rigor, replicability, and validity of results when evaluating studies that use LLMs to automate data processing or simulate human data. We offer practical advice on assessing the appropriateness of LLM applications in submitted studies, emphasizing the need for transparency in methodological reporting and the challenges posed by the non-deterministic and continuously evolving nature of these models. By providing a framework for best practices and critical review, this primer aims to ensure high-quality, innovative research within the evolving landscape of social science studies using LLMs. |
| Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation | Research Article, Discussion Article | Transforming | February 10, 2025 | Preprint | Research Design, Science Communication, Other | Computer Science, Any Discipline | With the advent of large multimodal language models, science is now at a threshold of an AI-based technological transformation. Recently, a plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently. This includes all aspects of the research cycle, especially (1) searching for relevant literature; (2) generating research ideas and conducting experimentation; generating (3) text-based and (4) multimodal content (e.g., scientific figures and diagrams); and (5) AI-based automatic peer review. In this survey, we provide an in-depth overview over these exciting recent developments, which promise to fundamentally alter the scientific research process for good. Our survey covers the five aspects outlined above, indicating relevant datasets, methods and results (including evaluation) as well as limitations and scope for future research. Ethical concerns regarding shortcomings of these tools and potential for misuse (fake science, plagiarism, harms to research integrity) take a particularly prominent place in our discussion. We hope that our survey will not only become a reference guide for newcomers to the field but also a catalyst for new AI-based initiatives in the area of "AI4Science". |
| What Limits LLM-based Human Simulation: LLMs or Our Design? | Discussion Article | Simulations | February 9, 2025 | Preprint | Data Generation | Computer Science | We argue that advancing LLM-based human simulation requires addressing both LLM's inherent limitations and simulation framework design challenges. Recent studies have revealed significant gaps between LLM-based human simulations and real-world observations, highlighting these dual challenges. To address these gaps, we present a comprehensive analysis of LLM limitations and our design issues, proposing targeted solutions for both aspects. Furthermore, we explore future directions that address both challenges simultaneously, particularly in data collection, LLM generation, and evaluation. To support further research in this field, we provide a curated collection of LLM-based human simulation resource |
| Artificial intelligence and illusions of understanding in scientific research | Discussion Article | Illusions | January 16, 2025 | Open Source | Research Design, Other | Any Discipline | Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI. |
| Engineering of Inquiry: The “Transformation” of Social Science through Generative AI | Discussion Article | Social | January 10, 2025 | Preprint | Research Design, Other | Any Discipline | We increasingly read that generative AI will “transform” the social sciences, but little to no work has conceptualized the conditions necessary to fulfill such a promise. We review recent research on generative AI and evaluate its potential to reshape research practices. As the technology advances, generative AI could support various research tasks, including idea generation, data collection, and analysis. However, we discuss three challenges to an optimistic outlook that focuses solely on accelerating research through practical tools and reducing costs through inexpensive “synthetic” data. First, generative AI raises severe concerns about the validity of conclusions drawn from synthetic data about human populations. Second, possible efficiency gains in the research process may be partially offset by new problems introduced by the technology. Third, applications of generative AI have so far focused on enhancing existing methods, with limited efforts to harness the technology’s unique potential to simulate human behavior in social environments. Sociologists could use sociological theories and methods to develop “generative agents.” A new “trading zone” could emerge where social scientists, statisticians, and computer scientists develop new methodologies to facilitate innovative lines of inquiry and produce scientifically valid conclusions. |
| Contribution and Challenges of ChatGPT and Similar Generative Artificial Intelligence in Biochemistry, Genetics and Molecular Biology | Discussion Article | Contributions | January 9, 2025 | Preprint | Other | Biology | The incorporation of ChatGPT, an advanced natural language processing model, into the realms of biochemistry, genetics, and molecular biology has revolutionized research and communication within these fields. This study explores the impacts and obstacles associated with ChatGPT in these domains. ChatGPT has made substantial contributions to the accessibility and dissemination of knowledge in biochemistry, genetics, and molecular biology. It simplifies complex scientific literature, offers concise explanations, answers queries, and generates summaries, benefiting researchers, students, and practitioners. Furthermore, it fosters global collaboration by enabling discussions and knowledge sharing among scientists. One of the primary advantages of ChatGPT is its assistance in decoding intricate genomic and proteomic data. It aids in genetic sequence analysis, identifies potential disease markers, and provides suggestions for experimental designs. Additionally, ChatGPT can assist in composing and reviewing research papers, elevating the quality of scientific publications in these fields. However, despite its merits, ChatGPT encounters challenges in the context of biochemistry, genetics, and molecular biology. It may struggle to grasp highly specialized or novel research topics, potentially leading to the dissemination of inaccurate information if not used judiciously. Privacy concerns arise when discussing sensitive genetic or medical data. ChatGPT brings valuable advantages to the domains of biochemistry, genetics, and molecular biology by simplifying information access, promoting collaborative research, and aiding in data interpretation. Nevertheless, users must remain vigilant about potential inaccuracies and privacy issues. Addressing these challenges as technology evolves will be crucial to fully unlock ChatGPT's potential in advancing research and education within these critical scientific disciplines. |