Using AI/LLMs for detecting scientific errors

This study investigates the integration of large language models (LLMs) alongside computer algebra systems (CAS) in a mathematics laboratory for civil engineering students, examining their combined impact on problem‐solving and inquiry‐driven learning. The intervention was designed using the integrate LLMs alongside CAS (ILAC) approach, which structures the inquiry process into key phases, guiding students through exploration, hypothesis testing, and solution validation. Six structured activities were implemented and assessed using quantitative and qualitative methods. Findings reveal that LLMs enhanced conceptual understanding, clarified methodologies, and assisted with command syntax, while CAS ensured computational accuracy and result validation. Many students critically cross‐verified LLM‐generated results with CAS, though some relied solely on LLMs, highlighting the need for better guidance on tool usage. While LLMs fostered engagement, skepticism remained regarding their ability to address deeper mathematical deficiencies. The intervention led to moderate improvements in students' familiarity with AI tools, though its short duration and the use of general‐purpose LLMs limited perceived usefulness. To maximize educational benefits, future implementations should consider longer interventions, advanced training in prompt engineering, and tailored AI solutions.

This review explores the frontiers of large language models (LLMs) in psychological 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 aim to provide a detailed exploration of how LLMs are transforming psychological research. We discuss 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 ability to model patterns, cognition, and behavior similar to those observed in humans. Furthermore, we explore the ability of such models to generate coherent, contextually relevant text, offering innovative tools for literature reviews, hypothesis generation, experimental designs, experimental subjects, and data analysis in psychology. We emphasize the importance of addressing technical and ethical challenges, including data privacy, the ethics of using LLMs in psychological research, and the need for a deeper understanding of these models’ limitations. Researchers should use LLMs responsibly in psychological studies, adhering to ethical standards and considering the potential consequences of deploying these technologies in sensitive areas. Overall, this review provides a comprehensive overview of the current state of LLMs in psychology, exploring the potential benefits and challenges. We hope it can serve as a call to action for researchers to responsibly leverage LLMs’ advantages while addressing the associated risks.

Leveraging few-shot learning and large language models for analyzing blood pressure variations across biological sex from scientific literature.

The use of Natural Language Processing (NLP) in the field of engineering seems complex with many applications which require calculations and engineering judgements. Large Language Models (LLM) have been successfully presented to be one of the most effective solutions in many specific fields such as arts and literature, to finance, to even software development. However, the use of LLM in the field of Civil Engineering and specifically in Geotechnical Engineering has not been discussed often compared to the other fields of expertise that utilize the functionalities of Application Programming Interfaces (API) of OpenAI. This paper aims to cover the details of the framework and breakdown of how LLMs can be used to train a domain-specific dataset, specifically related to settlement analysis, ground improvement, to slope stability analysis and provide results that are generated from a fine-tuned model with the dataset of research papers and technical documents provided by geotechnical engineers. The paper presents three folds: introduction for LLM models with pre-trained models, and choice of the models used. Then, the paper discusses the fine-tuning process of the LLM model with a domain-specific database. The paper provides benchmarks and metrics for performance of the model using the technique called Recall-Oriented Understudy for Gisting Evaluation (ROGUE) for validation of the outputs by comparing the result with prompt-completion pairs. With sufficient data for the prompt-completion pairs from the experts in the field and also from the scientific literature and localized technical documents, the LLM model trained with data related to the geotechnical engineering domain provides reasonable responses that otherwise would not have been achieved with the original ChatGPT LLM model. This paper introduces innovative way to manage current database of geotechnical documents and literature that can be optimized to be used with LLMs. The paper provides discussion of the comparative results and the future potential capabilities of its fine-tuned model.

BackgroundRecognizing patient symptoms is fundamental to medicine, research, and public health. However, symptoms are often underreported in coded formats even though they are routinely documented in physician notes. Large language models (LLMs), noted for their generalizability, could help bridge this gap by mimicking the role of human expert chart reviewers for symptom identification.ObjectiveThe primary objective of this multisite study was to measure the accurate identification of infectious respiratory disease symptoms using LLMs instructed to follow chart review guidelines. The secondary objective was to evaluate LLM generalizability in multisite settings without the need for site-specific training, fine-tuning, or customization.MethodsFour LLMs were evaluated: GPT-4, GPT-3.5, Llama2 70B, and Mixtral 8×7B. LLM prompts were instructed to take on the role of chart reviewers and follow symptom annotation guidelines when assessing physician notes. Ground truth labels for each note were annotated by subject matter experts. Optimal LLM prompting strategies were selected using a development corpus of 103 notes from the emergency department at Boston Children’s Hospital. The performance of each LLM was measured using a test corpus with 202 notes from Boston Children’s Hospital. The performance of an International Classification of Diseases, Tenth Revision (ICD-10)–based method was also measured as a baseline. Generalizability of the most performant LLM was then measured in a validation corpus of 308 notes from 21 emergency departments in the Indiana Health Information Exchange.ResultsSymptom identification accuracy was superior for every LLM tested for each infectious disease symptom compared to an ICD-10–based method (F1-score=45.1%). GPT-4 was the highest scoring (F1-score=91.4%; P<.001) and was significantly better than the ICD-10–based method, followed by GPT-3.5 (F1-score=90.0%; P<.001), Llama2 (F1-score=81.7%; P<.001), and Mixtral (F1-score=83.5%; P<.001). For the validation corpus, performance of the ICD-10–based method decreased (F1-score=26.9%), while GPT-4 increased (F1-score=94.0%), demonstrating better generalizability using GPT-4 (P<.001).ConclusionsLLMs significantly outperformed an ICD-10–based method for respiratory symptom identification in emergency department electronic health records. GPT-4 demonstrated the highest accuracy and generalizability, suggesting that LLMs may augment or replace traditional approaches. LLMs can be instructed to mimic human chart reviewers with high accuracy. Future work should assess broader symptom types and health care settings.

As large language models (LLMs) continue to advance, they have garnered widespread public attention and extensive application across numerous industries and academic disciplines. The proliferation of LLMs has sparked considerable research interest, with studies primarily focusing on their technical characteristics and specific application scenarios. However, systematic research examining the impact of LLMs on academic integrity remains relatively scarce. Academic integrity is of paramount importance in the biomedical field. Therefore, this paper aims to examine both the opportunities and challenges that LLMs present to academic integrity in the biomedical field, and proposes solutions for optimizing the beneficial applications of LLMs. From a positive perspective, LLMs offer substantial benefits to researchers by enhancing research efficiency, improving research quality, and facilitating the generation and dissemination of academic insights. However, they also present numerous challenges, including the potential for promoting academic misconduct, generating content inaccuracies or ambiguous expressions, introducing bias and fairness concerns, compromising peer review mechanisms, facilitating the dissemination of misinformation, and undermining higher education - all of which demand careful attention. To address these issues, we propose solutions and feasible strategies centered on ten core dimensions: establishing policies and regulatory guidelines, enhancing AI literacy and application capabilities, developing and improving relevant technical tools, establishing human-AI collaboration models, reforming peer review procedures and academic evaluation systems, promoting international cooperation and standardization, increasing transparency and strengthening disclosure, reinforcing professional ethics education, and advancing artificial intelligence detection technologies. Overall, while LLMs undoubtedly pose challenges for maintaining academic integrity, their potential for positive impact remains promising. It is anticipated that with technological advancement and improved ethical standards, LLMs will ultimately preserve and strengthen academic integrity.

This paper examines the potential of integrating Large Language Models (LLMs) with knowledge bases to imrpove the accuracy and reliability of their responses. The advantages of such a combination are evaluated, particularly in reducing the risk of hallucinations – the phenomenon where models generate erroneous or fabricated information. Various methodologies for combining LLMs with knowledge bases are analyzed, along with their respective advantages and limitations. The prospects and challenges of implementing this technology in diverse fields—such as information retrieval, decision support, and automated content creation—are discussed. The paper presents an overview of the current state of research in this domain and delineates directions for future investigation.The integration of LLMs with knowledge bases represents a significant advancement in artificial intelligence, aiming one of the key concerns regarding LLMs—their tendency to generate inaccurate or fabricated information, commonly referred to as hallucinations. This approach leverages the vast language understanding and generation capabilities of LLMs while grounding their outputs in structured and verified information from knowledge bases. The synergy between these two technologies has the potential to significantly enhance the reliability and factual accuracy of AI-generated responses across a wide range of applications. The methodologies for combining LLMs with knowledge bases differ in their implementation and effectiveness. Some approaches involve pre-training LLMs on curated knowledge bases, while others reference knowledge bases externally during the inference process. Each method presents its own set of advantages and challenges, such as balancing computational efficiency against accuracy and maintaining the fluency of LLM outputs while adhering strictly to factual information. The application of this integrated technology extends beyond mere information retrieval, showing promise in complex decision support systems, automated content creation for specialized domains, and contributing to the advancement of explainable AI by providing traceable sources for generated information. As research in this area progresses, it is expected to open new avenues for developing more trustworthy and capable AI systems across various industries and academic disciplines.

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate , where to evaluate , and how to evaluate . Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, education, natural and social sciences, agent applications, and other areas. Secondly, we answer the ‘where’ and ‘how’ questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing the performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey

With the advent of large language models (LLMs), managing scientific literature via LLMs has become a promising direction of research. However, existing approaches often overlook the rich structural and semantic relevance among scientific literature, limiting their ability to discern the relationships between pieces of scientific knowledge, and suffer from various types of hallucinations. These methods also focus narrowly on individual downstream tasks, limiting their applicability across use cases. We propose LitFM, the first literature foundation model designed for a wide variety of practical downstream tasks on domain-specific literature, with a focus on citation information. At its core, LitFM contains a novel graph retriever that can provide accurate and diverse recommendations for LLM to integrate graph structure information and relevant literature. LitFM also leverages a knowledge-infused LLM, fine-tuned through a well-developed instruction paradigm. It enables LitFM to extract domain-specific knowledge from literature and reason relationships among them. By integrating citation graphs during both training and inference, LitFM can generalize to unseen papers and accurately assess their relevance within existing literature. Additionally, we introduce new large-scale literature citation benchmark datasets on three academic fields, featuring sentence-level citation information and local context. Extensive experiments validate the superiority of LitFM, achieving 28.1% improvement on retrieval task in precision, and an average improvement of 7.52% over state-of-the-art across six downstream literature-related tasks.

Food extrusion is a widely used processing technique that transforms raw ingredients into structured food products-foods with well-defined textures, shapes, and functionalities-through mechanical shear and thermal energy. Despite its broad industrial application, the absence of a standardised, structured dataset capturing extrusion research parameters has hindered research synthesis, product development, and process optimisation. To address this gap, we introduce a manually curated food extrusion literature dataset capturing publication details, product types, process parameters, formulation data, experimental variables, characterisation metrics, and study-level insights. However, while manually curated datasets are typically of high quality, their scope is limited by time and resource constraints. We propose a method to supplement the dataset using large language models (LLMs) and evaluate the accuracy of LLMs in extracting structured food extrusion data from the scientific literature. Our findings demonstrate that LLMs can effectively extract structured information. However, some challenges, such as hallucination and missing contextual details, remain, suggesting that human effort can be spent on validating the resulting data. This still represents significant time savings as validation is a less time-consuming task than data extraction. We argue that LLMs thus represent a viable tool in providing supplementary datasets, and we propose a method to leverage existing human efforts in dataset creation to improve data quality.

Large language models (LLMs) have shown impressive ability in biomedical question-answering, but have not been adequately investigated for more specific biomedical applications. This study investigates ChatGPT family of models (GPT-3.5, GPT-4) in biomedical tasks beyond question-answering. We evaluated model performance with 11 122 samples for two fundamental tasks in the biomedical domain-classification (n = 8676) and reasoning (n = 2446). The first task involves classifying health advice in scientific literature, while the second task is detecting causal relations in biomedical literature. We used 20% of the dataset for prompt development, including zero- and few-shot settings with and without chain-of-thought (CoT). We then evaluated the best prompts from each setting on the remaining dataset, comparing them to models using simple features (BoW with logistic regression) and fine-tuned BioBERT models. Fine-tuning BioBERT produced the best classification (F1: 0.800-0.902) and reasoning (F1: 0.851) results. Among LLM approaches, few-shot CoT achieved the best classification (F1: 0.671-0.770) and reasoning (F1: 0.682) results, comparable to the BoW model (F1: 0.602-0.753 and 0.675 for classification and reasoning, respectively). It took 78 h to obtain the best LLM results, compared to 0.078 and 0.008 h for the top-performing BioBERT and BoW models, respectively. The simple BoW model performed similarly to the most complex LLM prompting. Prompt engineering required significant investment. Despite the excitement around viral ChatGPT, fine-tuning for two fundamental biomedical natural language processing tasks remained the best strategy.

Problem definition: Large language models (LLMs) are being increasingly leveraged in business and consumer decision-making processes. Because LLMs learn from human data and feedback, which can be biased, determining whether LLMs exhibit human-like behavioral decision biases (e.g., base-rate neglect, risk aversion, confirmation bias, etc.) is crucial prior to implementing LLMs into decision-making contexts and workflows. To understand this, we examine 18 common human biases that are important in operations management (OM) using the dominant LLM, ChatGPT. Methodology/results: We perform experiments where GPT-3.5 and GPT-4 act as participants to test these biases using vignettes adapted from the literature (“standard context”) and variants reframed in inventory and general OM contexts. In almost half of the experiments, Generative Pre-trained Transformer (GPT) mirrors human biases, diverging from prototypical human responses in the remaining experiments. We also observe that GPT models have a notable level of consistency between the standard and OM-specific experiments as well as across temporal versions of the GPT-3.5 model. Our comparative analysis between GPT-3.5 and GPT-4 reveals a dual-edged progression of GPT’s decision making, wherein GPT-4 advances in decision-making accuracy for problems with well-defined mathematical solutions while simultaneously displaying increased behavioral biases for preference-based problems. Managerial implications: First, our results highlight that managers will obtain the greatest benefits from deploying GPT to workflows leveraging established formulas. Second, that GPT displayed a high level of response consistency across the standard, inventory, and non-inventory operational contexts provides optimism that LLMs can offer reliable support even when details of the decision and problem contexts change. Third, although selecting between models, like GPT-3.5 and GPT-4, represents a trade-off in cost and performance, our results suggest that managers should invest in higher-performing models, particularly for solving problems with objective solutions. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada [Grant SSHRC 430-2019-00505]. The authors also gratefully acknowledge the Smith School of Business at Queen’s University for providing funding to support Y. Chen’s postdoctoral appointment. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0279 .

Integrating artificial intelligence (AI), particularly large language models (LLMs), into the healthcare industry is revolutionizing the field of medicine. LLMs possess the capability to analyze the scientific literature and genomic data by comprehending and producing human-like text. This enhances the accuracy, precision, and efficiency of extensive genomic analyses through contextualization. LLMs have made significant advancements in their ability to understand complex genetic terminology and accurately predict medical outcomes. These capabilities allow for a more thorough understanding of genetic influences on health issues and the creation of more effective therapies. This review emphasizes LLMs' significant impact on healthcare, evaluates their triumphs and limitations in genomic data processing, and makes recommendations for addressing these limitations in order to enhance the healthcare system. It explores the latest advancements in LLMs for genomic analysis, focusing on enhancing disease diagnosis and treatment accuracy by taking into account an individual's genetic composition. It also anticipates a future in which AI-driven genomic analysis is commonplace in clinical practice, suggesting potential research areas. To effectively leverage LLMs' potential in personalized medicine, it is vital to actively support innovation across multiple sectors, ensuring that AI developments directly contribute to healthcare solutions tailored to individual patients.

Can artificial-intelligence (AI) systems, such as large language models (LLMs), replace human participants in behavioral and psychological research? Here, I critically evaluate the replacement perspective and identify six interpretive fallacies that undermine its validity. These fallacies are (a) equating token prediction with human intelligence, (b) treating LLMs as the average human, (c) interpreting alignment as explanation, (d) anthropomorphizing AI systems, (e) essentializing identities, and (f) substituting model data for human evidence. Each fallacy represents a potential misunderstanding about what LLMs are and what they can tell researchers about human cognition. In the analysis, I distinguish levels of similarity between LLMs and humans, particularly functional equivalence (outputs) versus mechanistic equivalence (processes), while highlighting both technical limitations (addressable through engineering) and conceptual limitations (arising from fundamental differences between statistical and biological intelligence). For each fallacy, specific safeguards are provided to guide responsible research practices. Ultimately, the analysis supports conceptualizing LLMs as pragmatic simulation tools—useful for role-play, rapid hypothesis testing, and computational modeling (provided their outputs are validated against human data)—rather than as replacements for human participants. This framework enables researchers to leverage language models productively while respecting the fundamental differences between machine intelligence and human thought.

The use of large language models (LLMs) has exploded since November 2022, but there is sparse evidence regarding LLM use in health, medical, and research contexts. We aimed to summarise the current uses of and attitudes towards LLMs across our campus’ clinical, research, and teaching sites. We administered a survey about LLM uses and attitudes. We conducted summary quantitative analysis and inductive qualitative analysis of free text responses. In August–September 2023, we circulated the survey amongst all staff and students across our three campus sites (approximately n = 7500), comprising a paediatric academic hospital, research institute, and paediatric university department. We received 281 anonymous survey responses. We asked about participants’ knowledge of LLMs, their current use of LLMs in professional or learning contexts, and perspectives on possible future uses, opportunities, and risks of LLM use. Over 90% of respondents have heard of LLM tools and about two-thirds have used them in their work on our campus. Respondents reported using LLMs for various uses, including generating or editing text and exploring ideas. Many, but not necessarily all, respondents seem aware of the limitations and potential risks of LLMs, including privacy and security risks. Various respondents expressed enthusiasm about the opportunities of LLM use, including increased efficiency. Our findings show LLM tools are already widely used on our campus. Guidelines and governance are needed to keep up with practice. Insights from this survey were used to develop recommendations for the use of LLMs on our campus.