Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs

Large language models (LLMs) often struggle with factual accuracy in knowledge-intensive domains like healthcare. We introduce LEAF (Learning and Evaluation Augmented by Fact-Checking), a framework for improving LLM factuality in medical question answering. LEAF comprises three components: (1) RAFE, a robust fact-checking system using open-source LLMs and domain-specific retrieval to evaluate response accuracy; (2) Fact-Check-then-RAG, which leverages fact-checking results to guide retrieval without parameter updates; and (3) Learning from Fact Check, enabling self-training through supervised fine-tuning or preference-based learning using fact-checking as pseudo-labels. Experimental results show that RAFE outperforms Factcheck-GPT in detecting inaccuracies, Fact-Check-then-RAG effectively corrects errors, and Learning from Fact Check improves performance without labeled data. In a real-world healthcare deployment with proprietary medical documents, LEAF achieved an 83% improvement in factuality scores, demonstrating practical applicability for adapting general-purpose LLMs to organization-specific knowledge. Our framework provides a scalable solution for industrial applications requiring high factual accuracy.

Aim/Purpose: The study investigates the factors influencing the acceptance and utilisation of large language models (LLMs) (predictor variables of LLM usage), such as ChatGPT, in Learning design by instructional designers and university-teaching academics from various countries. Background: Large language models (LLMs) have exploded onto the scene, transforming the landscape of learning design. Instructional designers and university teaching academics have been overburdened with content creation for their teaching programmes, and the arrival of LLM models will help in this regard by developing more interactive content that drives student engagement and, in turn, contributes to student success. Since LLMs are a relatively new phenomenon, little is known about the factors influencing their acceptance in learning design; therefore, this research is needed, as learning design principles are the bedrock of student engagement and success. Methodology: A cross-sectional correlational quantitative study was employed. Data was collected using an online questionnaire posted on social media, including LinkedIn, from 203 instructional designers and university teaching academics. Purposive and snowball sampling methods were used to target instructional designers and university teaching academics at colleges and universities worldwide. Participants were asked to share the survey link with fellow instructional designers and university-teaching academics in their communities. The factor structure of the data was determined using exploratory factor analysis. Nonetheless, the factor structure derived from the LLMs did not entirely reflect the original configuration of the Unified Theory of Acceptance and Use of Technology (UTAUT3), as certain predictors appeared to coalesce, indicating LLMs’ unique nature in learning design. Confirmatory factor analysis was used to verify the fit of the data on the measurement model. First-order and second-order structural modelling were used to identify the structural relationships among the variables. Contribution: The study determines significant factors for the acceptance of LLMs by instructional designers and academic teaching staff in learning design, enabling possible opportunities for best practices in the field through interventions to optimize LLM usage. The study applies the technology acceptance model to the emerging LLM technology and extends the technology acceptance model by adding the trust construct as a predictor variable. Findings: The structural analysis results indicated that the ingrained LLM practices, LLM peer-driven expectations, innovative propensity towards LLM adoption, reliability and provider trust in LLMs, and ease of use and support influenced perceived LLM benefits and usage, but community standards and infrastructure had no influence. The second-order structural equation modelling indicated that perceived LLM benefits and usage and ingrained LLM habits contributed most to the learning design. Recommendations for Practitioners: Teaching academics and instructional designers must use LLMs in designing content, assessments, and interactive learning activities, and attend LLM training workshops on prompting and best practices in integrating LLMs into learning and teaching to see their benefits; hence, regular use of LLMs will then lead to trust and innovation in LLMs usage, enhancing learning design and improving student learning outcomes. Recommendation for Researchers: Researchers must use mixed methods approaches to have a deeper understanding of the factors influencing LLMs. Since habit and perceived LLM benefits and usage contributed the most variance to learning design, researchers must investigate strategies that optimise these factors in learning design, such as effective intervention strategies that can help form positive LLM habits. In addition, the findings provide researchers with a starting point for future research. Further researchers must investigate interventions that optimise the influence of personal innovativeness and trust that contributed the least variance to learning design, hence unlocking the potential of LLMs in learning design through innovation, responsible, and ethical use. Impact on Society: The use of LLMs in learning design has a high possibility of transforming education, specifically the learning design landscape. Using LLMs will free up more time for teaching academics and instructional designers so that they spend more time on higher-order thinking skill demands. Consequently, the students will be exposed to more engaging and interactive content, resulting in improved learning outcomes. Future Research: Future research must include context-derived external variables in technology acceptance models, such as levels of prompting competencies, to provide a deeper understanding of LLMs. In addition, future research must be based on the application and impact of LLMs on student engagement and success, and their attainment of 21st-century skills.

Model tuning or prompt Tuning? a study of large language models for clinical concept and relation extraction

Large language models (LLMs) such as ChatGPT have shown significant potential in radiology. Their effectiveness often depends on prompt engineering, which optimizes the interaction with the chatbot for accurate results. Here, we highlight the critical role of prompt engineering in tailoring the LLMs' responses to specific medical tasks. Using a clinical case, we elucidate different prompting strategies to adapt the LLM ChatGPT using GPT4 to new tasks without additional training of the base model. These approaches range from precision prompts to advanced in-context methods such as few-shot and zero-shot learning. Additionally, the significance of embeddings, which serve as a data representation technique, is discussed. Prompt engineering substantially improved and focused the chatbot's output. Moreover, embedding of specialized knowledge allows for more transparent insight into the model's decision-making and thus enhances trust. Despite certain challenges, prompt engineering plays a pivotal role in harnessing the potential of LLMs for specialized tasks in the medical domain, particularly radiology. As LLMs continue to evolve, techniques like few-shot learning, zero-shot learning, and embedding-based retrieval mechanisms will become indispensable in delivering tailored outputs. · Large language models might impact radiological practice and decision-masking.. · However, implementation and performance are dependent on the assigned task.. · Optimization of prompting strategies can substantially improve model performance.. · Strategies for prompt engineering range from precision prompts to zero-shot learning.. · Russe MF, Reisert M, Bamberg F et al. Improving the use of LLMs in radiology through prompt engineering: from precision prompts to zero-shot learning . Fortschr Röntgenstr 2024; 196: 1166 - 1170.

Evaluating gpt-4 for zero-shot classification of bleeding and clotting events: Can large language models serve as second reviewers?

Purpose This study aims to explore the integration of large language models (LLMs) and vision-language models (VLMs) in robotics, highlighting their potential benefits and the safety challenges they introduce, including robustness issues, adversarial vulnerabilities, privacy concerns and ethical implications. Design/methodology/approach This survey conducts a comprehensive analysis of the safety risks associated with LLM- and VLM-powered robotic systems. The authors review existing literature, analyze key challenges, evaluate current mitigation strategies and propose future research directions. Findings The study identifies that ensuring the safety of LLM-/VLM-driven robots requires a multi-faceted approach. While current mitigation strategies address certain risks, gaps remain in real-time monitoring, adversarial robustness and ethical safeguards. Originality/value This study offers a structured and comprehensive overview of the safety challenges in LLM-/VLM-driven robotics. It contributes to ongoing discussions by integrating technical, ethical and regulatory perspectives to guide future advancements in safe and responsible artificial intelligence-driven robotics.

The paper studies the problem of software code migration during the transition from the old version of the SAP ERP 6.0 system to modern ERP platforms, in particular SAP S/4HANA. The necessity of automation of the migration process using artificial intelligence methods and tools is substantiated. A comprehensive analysis of existing academic and industrial case studies confirmed the feasibility of using large language models for code migration. A number of advantages of the approach based on large language models are revealed, in particular, the ability to handle various migration scenarios. The key risks and limitations common in the enterprise environment are highlighted, including data security, privacy, high cost of API calls and limited knowledge of models regarding the subject area. A method is developed for selecting a large open source language model that can be safely used in the enterprise landscape, thereby mitigating the risks associated with cloud solutions. The method is based on the developed system of criteria, including: model size, context window size, license type, support for fine-tuning training, focus on code quality (measured using a reference dataset), support for model quantization and community support. The hybrid AHP-TOPSIS method is proposed for systematic ranking of candidate models. The AHP method was used to check the weights of the criteria, while TOPSIS was used to rank the models based on their proximity to the ideal solution. Based on the developed method, three large language models were selected: Qwen 2.5 Coder 14B, DeepSeek-Coder-V2 16B and Llama 3.1 8B. A comprehensive testing plan for the selected models was developed for further experimental studies. The scientific novelty of the work lies in the development of a method for selecting large language models for custom code migration tasks, which differs from the existing system of model selection criteria and the use of a hybrid AHP-TOPSIS approach for ranking candidate models. The practical value of the work is that the developed method allows you to select a model taking into account the constraints of the corporate environment and information security requirements, which will increase the level of automation in the migration of AVAP when switching to new versions of ERP systems from SAP SE. The developed method can be applied to the selection of large language models when upgrading any large computer systems developed in both open source and proprietary programming languages.

Readability & quality of large language model responses in CAR-T patient education

Picture description tasks, such as the Cookie Theft task, are widely used in neuropsychological assessments to detect cognitive impairment. However, manual scoring is time-consuming, requires specialized training, and is subject to interrater variability. Recent advancements in natural language processing, particularly large language models (LLMs), offer a promising solution to automate and standardize the scoring process. This study evaluated the performance and reliability of five LLMs (GPT-4 Turbo, GPT-4o, Claude 3 Opus, Claude 3 Sonnet, and Llama 3 70b) in scoring the Cookie Theft picture description task. A subset of 25 participants were selected from the DementiaBank corpus. The LLMs were tasked with scoring 22 content units in the participants' responses using various prompt strategies, including few-shot learning, prompt chaining, and self-consistency. LLM performance was compared to the consensus score of three human raters. LLMs demonstrated comparable accuracy to human raters in scoring the Cookie Theft task, with no significant differences in mean absolute error (MAE) between the best performing models and human raters. Few-shot learning significantly improved LLM performance, while prompt chaining and self-consistency showed limited benefits. Claude 3 Opus and GPT-4o exhibited the highest accuracy and reliability. Notably, LLMs showed significantly higher interrater reliability compared to human raters. The findings demonstrate the potential of LLMs to accurately and reliably score picture description tasks, offering a promising approach to streamline and standardize neuropsychological assessments. By automating the scoring process, clinicians and researchers can benefit from increased efficiency, reduced subjectivity, and improved scalability in evaluating cognitive functions.

Large Language Models (LLMs) have ushered in a transformative era in Natural Language Processing (NLP), reshaping research and extending NLP's influence to other fields of study. However, there is little to no work examining the degree to which LLMs influence other research fields. This work empirically and systematically examines the influence and use of LLMs in fields beyond NLP. We curate 106 LLMs and analyze ∼148k papers citing LLMs to quantify their influence and reveal trends in their usage patterns. Our analysis reveals not only the increasing prevalence of LLMs in non-CS fields but also the disparities in their usage, with some fields utilizing them more frequently than others since 2018, notably Linguistics and Engineering together accounting for ∼45% of LLM citations. Our findings further indicate that most of these fields predominantly employ task-agnostic LLMs, proficient in zero or few-shot learning without requiring further fine-tuning, to address their domain-specific problems. This study sheds light on the cross-disciplinary impact of NLP through LLMs, providing a better understanding of the opportunities and challenges.

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.

LLMs application to clinical practice is growing fast. However, LLMs are less studied in geriatrics practice but are urgently needed. This symposium will address whether LLMs allocation to geriatric practice can be trusted via five approaches. 1) LLMs generated gender and race-biased outputs. We will demonstrate whether LLMs generated age-biased output by assessing their geriatric attitude evaluated by social workers. 2). LLMs passed USMLE and other examinations. We will demonstrate whether LLMs can pass geriatrics knowledge competence tests evaluated by geriatricians 3). LLMs performed well on clinical vignettes from different clinical disciplines. We will demonstrate whether LLMs can perform well on geriatrics 5M-based vignettes of older adults evaluated by clinical providers and trainees 4) LLMs reviewed and summarized clinical charts. We will demonstrate whether LLMs can review geriatrics and general medicine notes to extract Mobility (one of Geriatrics 5Ms) documentation evaluated by geriatricians 5). LLMs can generate deprescribing recommendations, tapering schedules, and patient education materials. We will demonstrate their accuracy, safety, and appropriateness compared to recommendations from a multidisciplinary team of pharmacists, geriatricians, and nurses. Specifically, this symposium will address the following topics: 1) Geriatric Attitude of ChatGPT4.o and Its Evaluation by Social Workers. 2) ChatGPT4.o Geriatrics Knowledge Competency and Its Evaluation by Geriatricians. 3) LLMs application to geriatrics 5Ms evaluated by clinical providers and trainees. 4) Using LLMs to Extract and Assess Mobility Documentation for Age-Friendly Health System evaluated by geriatricians. 5) Using LLMs to generate medication deprescribing recommendations compared to clinician-led deprescribing recommendations.

Introduction Advances in large language models (LLMs) have opened new avenues for healthcare applications. Recently, ChatGPT-4 successfully achieved the pass mark >80% in 5 of 10 sleep medicine examination domains, indicating a strong foundational knowledge of sleep medicine. However, the ability to answer USMLE-type multiple-choice questions may not equate to the capacity to offer accurate and comprehensive answers to clinical queries or real-world case scenarios. Current literature exhibits a significant gap in validation research examining the clinical utility of LLMs in evidence-based medicine practice. This investigation aims to evaluate the potential usefulness and reliability of LLMs as adjunctive clinical decision-support tools (namely, “curbside consultants”) in sleep medicine. Methods Six clinical sleep queries and six case scenarios were presented to 6 LLMs including 3 general-purpose LLMs (ChatGPT-4o, Gemini-1.5-Pro, and Llama-3.1-405B) and 3 medical-specialized LLMs (OpenEvidence, Clara AI, and MediGPT). Performance assessment was conducted independently utilizing two independently developed 5-point Likert scales evaluating two primary domains: answer content (accuracy, relevance, comprehensiveness/depth, clarity/coherence, and unique insightfulness) and reference quality (accuracy, relevance, currentness, comprehensiveness/depth, and searchability). Benchmark answers were established through consensus among four sleep medicine specialists. Analysis of variance was used to compare the performance of the LLMs. Results The medical LLMs demonstrated superior overall performance compared to the general LLMs (P< 0.001). The primary distinction was observed in reference quality metrics, where medical LLMs significantly outperformed general LLMs across all parameters: accuracy, relevance, and searchability (p< 0.001), currentness (p=0.001), and comprehensiveness/depth (p=0.010). Notably, Open Evidence achieved the highest reference quality (p< 0.001). In contrast, the answer content analysis revealed no significant overall differences between medical and general LLMs (p=0.659). Most answer content-related metrics, including accuracy, relevance, or unique insightfulness, did not differ significantly (p>0.050). An exception was clarify/coherence, where medical LLMs were superior to general LLMs (p=0.030). Furthermore, MediGPT and ChatGPT-4o displayed better content comprehensiveness/depth relative to other LLMs (p< 0.001). These two LLMs exhibited comparable performance in overall metrics (p=0.615), answer contents (p=0.922), and reference quality (p=0.621). Conclusion Both medical-specialized and general-purpose LLMs show promise as adjunctive decision-support tools in clinical practice. However, substantial improvements in reference quality are critically needed across most LLM platforms. Support (if any)

Large Language Models (LLMs) have shown impressive capabilities, yet they still struggle with math reasoning. In this work, we propose CoT-Influx, a novel approach that pushes the boundary of few-shot Chain-of-Thoughts (CoT) learning to improve LLM mathematical reasoning. Motivated by the observation that adding more concise CoT examples in the prompt can improve LLM reasoning performance, CoT-Influx employs a coarse-to-fine pruner to maximize the input of effective and concise CoT examples. The pruner first selects as many crucial CoT examples as possible and then prunes unimportant tokens to fit the context window. A math reasoning dataset with diverse difficulty levels and reasoning steps is used to train the pruner, along with a math-specialized reinforcement learning approach. As a result, by enabling more CoT examples with double the context window size in tokens, CoT-Influx significantly outperforms various prompting baselines across various LLMs (LLaMA2-7B, 13B, 70B) and 6 math datasets, achieving up to 4.40% absolute improvements. Remarkably, without any fine-tuning, LLaMA2-70B with CoT-Influx surpasses GPT-3.5 and a wide range of larger LLMs (PaLM, Minerva 540B, etc.) on GSM8K. CoT-Influx is a plug-and-play module for LLMs, adaptable in various scenarios. It's compatible with advanced reasoning prompting techniques, such as self-consistency, and supports different long-context LLMs, including Mistral-7B-v0.3-32K and Yi-6B-200K. Codes are available at https://github.com/HuangOwen/CoT-Influx.

In today’s rapidly evolving business landscape, Artificial Intelligence (AI), and specifically Large Language Models (LLMs), are redefining how organizations operate, make decisions, and engage with customers. AI-driven technologies have become indispensable, providing businesses with powerful tools to streamline operations, derive actionable insights from vast data, and foster more meaningful customer interactions. For business leaders, scholars, and practitioners alike, understanding the transformative potential of AI isn’t just advantageous—it’s essential to staying competitive in an increasingly data-driven world.This editorial delves into recent scholarly advancements in LLM applications within business contexts, analyzing studies that explore AI’s potential across various domains, from decision support to creative industries. By introducing a structured framework, this editorial highlights key insights and contributions from recent studies, assessing their value to academia and industry. The following comparative analysis sheds light on how these innovations shape our understanding of AI’s role in business while pointing to future research directions.Puyt and Madsen's (2024) study stands out as a foundational exploration of LLM accuracy, assessing ChatGPT-4's ability to recount the history of the SWOT analysis-a vital business strategy tool. Their findings reveal that, while ChatGPT-4 effectively conveys general concepts, it struggles with detailed historical information, often producing inaccuracies or "hallucinations." This gap underscores the need for LLMs to be trained with verified academic data, particularly for strategic business applications that demand precision. This study not only contributes to the literature by proposing methods to evaluate AI accuracy in historical contexts but also highlights the importance of rigorous information vetting in industry settings where reliability is crucial.In contrast, Raikov et al. (2024) explore a hybrid intelligence model that combines LLM capabilities with explainable AI (XAI) principles to enhance human-machine collaboration. Their approach emphasizes cognitive semantics, improving transparency and decision-making efficiency. The hybrid model's real-time adaptability addresses the needs of complex, regulated industries such as finance and healthcare, where trust in AI decisions is paramount. Academically, this study provides a valuable addition to XAI literature by demonstrating how LLMs can bridge the gap between AI autonomy and human oversight, making it a model for future human-AI interactions in complex business environments.Another significant study by Mariotti and colleagues (2024) examines the integration of LLMs with enterprise knowledge graphs to enhance data-driven decision-making. By enabling organizations to leverage knowledge graphs for more accurate and scalable data retrieval, this research provides a robust framework for businesses seeking efficient knowledge management systems. The academic contribution here lies in advancing the dialogue between LLMs and knowledge graphs, emphasizing ethical data handling and quality standards essential for industry applications. For enterprises, the study offers practical solutions to achieve streamlined data management, balancing automation with privacy and security. 2024) take a different approach, investigating LLMs' role in creative industries, specifically within fashion design. They introduce a hybrid intelligence model that supports creative processes, allowing AI to complement rather than replace human ingenuity. While LLMs in this field demonstrate potential in automating repetitive design tasks and enhancing customer personalization, the study reveals limitations in AI's ability to handle spatial and stylistic nuances. This study's academic contribution lies in promoting human-AI co-creation, inspiring further research into AI applications across diverse creative sectors, including media and marketing.Collectively, these studies not only illuminate LLMs' transformative potential in business but also highlight critical ethical and operational considerations. Ensuring accuracy, transparency, and data privacy are vital to responsibly integrating AI into business workflows. Future research should focus on enhancing LLM accuracy, refining hybrid intelligence models, and exploring creative AI applications, all while maintaining ethical standards. As LLMs evolve, interdisciplinary collaborations will be essential to harness their full potential, making AI an ethical, effective, and innovative force in the business world.