Abstract: The use of large language models (LLMs) in bioethical, scientific, and medical writing remains controversial. While there is broad agreement in some circles that LLMs cannot count as authors, there is no consensus about whether and how humans using LLMs can count as authors. In many fields, authorship is distributed among large teams of researchers, some of whom -- including paradigmatic "senior authors" who guide and determine the scope of a project and ultimately vouch for its integrity -- may not write a singl e word. In this paper, we argue that LLM use (under specific conditions) is analogous to a form of senior authorship. On this view, the use of LLMs, even to generate complete drafts of research papers, can be considered a legitimate form of authorship according to the accepted criteria in many fields. We conclude that either such use should be recognized as legitimate, or current criteria for authorship require fundamental revision. AI use declaration: Chat GPT version 5 was used to help format Box 1. AI wa s not used for any other part of the preparation or writing of this manuscript. This is a pre print of a paper that has been submitted to a journal. It has not yet gone through peer review. Authorship Without Writing: Large Language Models and the "Senior Author" Analogy Clint Hurshman, Sebastian Porsdam Mann, Julian Savulescu, Brian D. Earp I. Introduction The use of large language models (LLMs) in bioethics as well as scientific and medical writing continues to be controversial. Thus far, there has been broad agreement -- for example, among medical publishers -- that LLMs cannot count as authors, but there is still no consensus about the status of LLM - assisted text production as a form of writing, and by extension, the status of LLM users as authors. Here, we contribute to this debate by exploring -- and drawing analogies to -- the collaborative nature of writing, and t he distributed character of authorship, in many domains of research.

The proliferation of Large Language Models (LLMs) in late 2022 has impacted academic writing, threatening credibility, and causing institutional uncertainty. We seek to determine the degree to which LLMs are used to generate critical text as opposed to being used for editing, such as checking for grammar errors or inappropriate phrasing. In our study, we analyze arXiv papers for stylistic segmentation, which we measure by varying a PELT threshold against a Bayesian classifier trained on GPT-regenerated text. We find that LLM-attributed language is not predictive of stylistic segmentation, suggesting that when authors use LLMs, they do so uniformly, reducing the risk of hallucinations being introduced into academic preprints.

Probabilistic classify-and-count, where we calibrated the model6 (see Appendix) and then averaged the LLM probabilities (estimate: 35.2% [29.8%, 40.6%]) Corrected classify-and-count, adjusting for the type I and type II error rates estimated on the training data18 (estimate: 35.4% [27.8%, 43.0%]). We validated our results by analyzing crowd workers' copy-pasting behavior (see Appendix), finding that 55% of the summaries where workers had copy-pasted text were classified as synthetic (that is, LLM probability above 50%) vs.

Trust plays a pivotal role in Lecturer-Student-Collaboration, encompassing teaching and research aspects. The advent of Large Language Models (LLMs) in platforms like Open AI's ChatGPT, coupled with their cost-effectiveness and high-quality results, has led to their rapid adoption among university students. However, discerning genuine student input from LLM-generated output poses a challenge for lecturers. This dilemma jeopardizes the trust relationship between lecturers and students, potentially impacting university downstream activities, particularly collaborative research initiatives. Despite attempts to establish guidelines for student LLM use, a clear framework mutually beneficial for lecturers and students in higher education remains elusive. This study addresses the research question: How does the use of LLMs by students impact Informational and Procedural Justice, influencing Team Trust and Expected Team Performance? Methodically, we applied a quantitative construct-based survey, evaluated using techniques of Structural Equation Modelling (PLS- SEM) to examine potential relationships among these constructs. Our findings based on 23 valid respondents from Ndejje University indicate that lecturers are less concerned about the fairness of LLM use per se but are more focused on the transparency of student utilization, which significantly influences Team Trust positively. This research contributes to the global discourse on integrating and regulating LLMs and subsequent models in education. We propose that guidelines should support LLM use while enforcing transparency in Lecturer-Student- Collaboration to foster Team Trust and Performance. The study contributes valuable insights for shaping policies enabling ethical and transparent LLMs usage in education to ensure effectiveness of collaborative learning environments.

The capability of large language models (LLMs) to generate, debug, and explain code has sparked the interest of researchers and educators in undergraduate programming, with many anticipating their transformative potential in programming education. However, decisions about why and how to use LLMs in programming education may involve more than just the assessment of an LLM's technical capabilities. Using the social shaping of technology theory as a guiding framework, our study explores how students' social perceptions influence their own LLM usage. We then examine the correlation of self-reported LLM usage with students' self-efficacy and midterm performances in an undergraduate programming course. Triangulating data from an anonymous end-of-course student survey (n = 158), a mid-course self-efficacy survey (n=158), student interviews (n = 10), self-reported LLM usage on homework, and midterm performances, we discovered that students' use of LLMs was associated with their expectations for their future careers and their perceptions of peer usage. Additionally, early self-reported LLM usage in our context correlated with lower self-efficacy and lower midterm scores, while students' perceived over-reliance on LLMs, rather than their usage itself, correlated with decreased self-efficacy later in the course.

We show that the use of large language models (LLMs) is prevalent among crowd workers, and that targeted mitigation strategies can significantly reduce, but not eliminate, LLM use. On a text summarization task where workers were not directed in any way regarding their LLM use, the estimated prevalence of LLM use was around 30%, but was reduced by about half by asking workers to not use LLMs and by raising the cost of using them, e.g., by disabling copy-pasting. Secondary analyses give further insight into LLM use and its prevention: LLM use yields high-quality but homogeneous responses, which may harm research concerned with human (rather than model) behavior and degrade future models trained with crowdsourced data. At the same time, preventing LLM use may be at odds with obtaining high-quality responses; e.g., when requesting workers not to use LLMs, summaries contained fewer keywords carrying essential information. Our estimates will likely change as LLMs increase in popularity or capabilities, and as norms around their usage change. Yet, understanding the co-evolution of LLM-based tools and users is key to maintaining the validity of research done using crowdsourcing, and we provide a critical baseline before widespread adoption ensues.

AI-powered chatbots such as ChatGPT and Google Bard are certainly having a moment--the next generation of conversational software tools promise to do everything from taking over our web searches to producing an endless supply of creative literature to remembering all the world's knowledge so we don't have to. ChatGPT, Google Bard, and other bots like them, are examples of large language models, or LLMs, and it's worth digging into how they work. It means you'll be able to better make use of them, and have a better appreciation of what they're good at (and what they really shouldn't be trusted with). Like a lot of artificial intelligence systems--like the ones designed to recognize your voice or generate cat pictures--LLMs are trained on huge amounts of data. The companies behind them have been rather circumspect when it comes to revealing where exactly that data comes from, but there are certain clues we can look at. For example, the research paper introducing the LaMDA (Language Model for Dialogue Applications) model, which Bard is built on, mentions Wikipedia, "public forums," and "code documents from sites related to programming like Q&A sites, tutorials, etc."