TIFD: Tibetan Instruction-Following Dataset for Large Language Models Supervised Fine-Tuning

Background and Aims Large language models (LLMs) have gained significant attention in the field of natural language processing (NLP), marking a shift from traditional techniques like Term Frequency-Inverse Document Frequency (TF-IDF). We developed a traditional NLP model to predict arteriovenous fistula (AVF) failure within next 30 days using clinical notes. The goal of this analysis was to investigate whether LLMs would outperform traditional NLP techniques, specifically in the context of predicting AVF failure within the next 30 days using clinical notes. Method We defined AVF failure as the change in status from active to permanently unusable status or temporarily unusable status. We used data from a large kidney care network from January 2021 to December 2021. Two models were created using LLMs and traditional TF-IDF technique. We used “distilbert-base-uncased”, a distilled version of BERT base model [1], and compared its performance with traditional TF-IDF-based NLP techniques. The dataset was randomly divided into 60% training, 20% validation and 20% test dataset. The test data, comprising of unseen patients’ data was used to evaluate the performance of the model. Both models were evaluated using metrics such as area under the receiver operating curve (AUROC), accuracy, sensitivity, and specificity. Results The incidence of 30 days AVF failure rate was 2.3% in the population. Both LLMs and traditional showed similar overall performance as summarized in Table 1. Notably, LLMs showed marginally better performance in certain evaluation metrics. Both models had same AUROC of 0.64 on test data. The accuracy and balanced accuracy for LLMs were 72.9% and 59.7%, respectively, compared to 70.9% and 59.6% for the traditional TF-IDF approach. In terms of specificity, LLMs scored 73.2%, slightly higher than the 71.2% observed for traditional NLP methods. However, LLMs had a lower sensitivity of 46.1% compared to 48% for traditional NLP. However, it is worth noting that training on LLMs took considerably longer than TF-IDF. Moreover, it also used higher computational resources such as utilization of graphics processing units (GPU) instances in cloud-based services, leading to higher cost. Conclusion In our study, we discovered that advanced LLMs perform comparably to traditional TF-IDF modeling techniques in predicting the failure of AVF. Both models demonstrated identical AUROC. While specificity was higher in LLMs compared to traditional NLP, sensitivity was higher in traditional NLP compared to LLMs. LLM was fine-tuned with a limited dataset, which could have influenced its performance to be similar to that of traditional NLP methods. This finding suggests that while LLMs may excel in certain scenarios, such as performing in-depth sentiment analysis of patient data for complex tasks, their effectiveness is highly dependent on the specific use case. It is crucial to weigh the benefits against the resources required for LLMs, as they can be significantly more resource-intensive and costly compared to traditional TF-IDF methods. This highlights the importance of a use-case-driven approach in selecting the appropriate NLP technique for healthcare applications.

Abstract. Large language models (LLMs) have revolutionized the field of natural language processing (NLP), demonstrating remarkable capabilities in understanding, generating, and manipulating human language. This comprehensive review explores the development, applications, optimizations, and challenges of LLMs. This paper begin by tracing the evolution of these models and their foundational architectures, such as the Transformer, GPT, and BERT. We then delve into the applications of LLMs in natural language understanding tasks, including sentiment analysis, named entity recognition, question answering, and text summarization, highlighting real-world use cases. Next, we examine the role of LLMs in natural language generation, covering areas such as content creation, language translation, personalized recommendations, and automated responses. We further discuss LLM applications in other NLP tasks like text style transfer, text correction, and language model pre-training. Subsequently, we explore techniques for optimizing and improving LLMs, including model compression, explainability, robustness, and security. Finally, we address the challenges posed by the significant computational requirements, sample inefficiency, and ethical considerations surrounding LLMs. We conclude by discussing potential future research directions, such as efficient architectures, few-shot learning, bias mitigation, and privacy-preserving techniques, which will shape the ongoing development and responsible deployment of LLMs in NLP.

Large language models (LLMs) have made significant advances in the field of natural language processing (NLP). However, their centralized training approach faces challenges related to data privacy, communication efficiency, and robustness against adversarial attacks, particularly in wireless environments. With the gradual depletion of high-quality public data, there is an urgent need to leverage private data distributed across various parties. Although federated learning (FL) offers a privacy-preserving collaborative training paradigm, it struggles to meet the high computational demands of edge devices and remains vulnerable to adversarial attacks. This paper introduces <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ROFED</small>-LLM, a novel framework for robust, privacy-preserving training of LLMs on decentralized private data over wireless networks. By integrating split federated learning, which partitions the model across devices to enhance privacy, with adaptive jamming defense mechanisms, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ROFED</small>-LLM enables collaborative LLM training without raw data sharing while ensuring resilience against wireless adversarial attacks. Our multi-modal defense strategy combines model-level protections, such as differential privacy and dynamic pruning, with communication-level safeguards, including adaptive beamforming which optimizes wireless signal transmission to mitigate interference, and resource allocation optimization. Extensive experiments across diverse NLP tasks demonstrate <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ROFED</small>-LLM's superiority, achieving a 12.87% improvement in privacy preservation and 18.26% enhancement in jamming resilience compared to existing methods such as FedAvg and SCAFFOLD, with only a marginal 3.94% trade-off in model accuracy. Our code repository has been open sourced at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://anonymous.4open.science/r/RoFed-LLM-54E1</uri>.

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

Recent years have witnessed notable progress in Language Model (LM) technology, especially with the introduction of Large Language Models (LLMs). With the use of complex neural networks and enormous volumes of data, these models—like the GPT (Generative Pre-trained Transformer) series—have completely changed the field of natural language processing. This study offers a thorough analysis of LLMs, examining their uses as well as the difficulties and moral issues that come with implementing LLMs in practical settings. Moreover, it investigates the wide range of uses of LLMs in many fields, such as question answering, drug discovery, language translation, and text production. By means of this review, we hope to offer ideas on future directions in this quickly developing field as well as insights into the state-of-the-art in LLM research today. Key Words: Large Language Models, LangChain, Natural language processing, GPT, ChatGPT, AI chatbots, Indirect Prompt Injection

BackgroundA large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications.ObjectiveThe aim of this adapted Delphi study was to collect researchers’ opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care.MethodsWe invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items.ResultsThe first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data.ConclusionsFuture research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice.

Continuous professional development of science teachers is closely related to their ability to reflect on their science lessons. For teachers to effectively reflect on their lesson, they need opportunities to make meaningful teaching-related experiences and receive guiding feedback for their reflections during initial teacher education. A sensible means to provide reflection opportunities for teachers is through written reflection assignments. However, assessing written reflections is conceptually challenging and resource-intensive. Recent developments suggest that artificial intelligence (AI) might provide novel opportunities for science teacher education and assessing written reflections. Consequently, we investigate ways in which AI in the form of a large language model can be utilized in a specific science teacher training program. The potential of AI for the use in higher education have been highlighted in recent years. However, it is noted that large language models oftentimes are too generic to be useful in specific science teacher education contexts. To address this issue, we used a pre-trained large language model and fine-tuned it to provide individualized feedback for pre-service physics teachers’ written reflection and investigated to which degree contextual factors impact the classification accuracy. Our results show that the pre-trained language model allowed a sufficient classification and that the classification accuracy can be improved by context specification. Our study highlights opportunities and challenges when utilizing AI, in particular large language models, for reflective writing analytics in science education.

BackgroundThe field of natural language processing (NLP) has evolved significantly since its inception in the 1950s, with large language models (LLMs) now playing a crucial role in addressing medical challenges.ObjectivesThis study evaluates the alignment of three prominent LLMs—Gemini, Copilot, and ChatGPT-4.0—with expert consensus on imaging recommendations for acute flank pain.MethodsA total of 29 clinical vignettes representing different combinations of age, sex, pregnancy status, likelihood of stone disease, and alternative diagnoses were posed to the three LLMs (Gemini, Copilot, and ChatGPT-4.0) between March and April 2024. Responses were compared to the consensus recommendations of a multispecialty panel. The primary outcome was the rate of LLM responses matching the majority consensus. Secondary outcomes included alignment with consensus-rated perfect (9/9) or excellent (8/9) responses and agreement with any of the nine panel members.ResultsGemini aligned with the majority consensus in 65.5% of cases, compared to 41.4% for both Copilot and ChatGPT-4.0. In scenarios rated as perfect or excellent by the consensus, Gemini showed 69.5% agreement, significantly higher than Copilot and ChatGPT-4.0, both at 43.4% (p = 0.045 and < 0.001, respectively). Overall, Gemini demonstrated an agreement rate of 82.7% with any of the nine reviewers, indicating superior capability in addressing complex medical inquiries.ConclusionGemini consistently outperformed Copilot and ChatGPT-4.0 in aligning with expert consensus, suggesting its potential as a reliable tool in clinical decision-making. Further research is needed to enhance the reliability and accuracy of LLMs and to address the ethical and legal challenges associated with their integration into healthcare systems.

Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of LLMs in downstream NLP applications effectively. This can be accomplished through a variety of approaches, including augmented prompting, instruction prompt tuning, and reinforcement learning from human feedback (RLHF). Our focus is on making LLMs accessible to the broader biomedical informatics community, including clinicians and researchers who may be unfamiliar with NLP. Additionally, NLP practitioners may gain insight from the described best practices. We focus on 3 broad categories of NLP tasks, namely natural language understanding, natural language inferencing, and natural language generation. We review the emerging trends in prompt tuning, instruction fine-tuning, and evaluation metrics used for LLMs while drawing attention to several issues that impact biomedical NLP applications, including falsehoods in generated text (confabulation/hallucinations), toxicity, and dataset contamination leading to overfitting. We also review potential approaches to address some of these current challenges in LLMs, such as chain of thought prompting, and the phenomena of emergent capabilities observed in LLMs that can be leveraged to address complex NLP challenge in biomedical applications.

With the advent of Large Language Models (LLMs) researchers outside the Natural Language Processing (NLP) field are interested in learning how to process textual data for their own domain research goals.They are particularly motivated to start experimenting directly with LLMs, implicitly neglecting the large amount of accumulated knowledge that NLP has to offer them.In this text, we briefly share our new lesson materials that aim to show aspiring practitioners the strong connection between NLP fundamentals and LLMs, in the form of a two-day workshop.Our training material is mainly aimed at graduate students outside the NLP sphere who have basic technical knowledge and wish to start working with text, is fully open source and available online 1 .

Large language models (LLMs) have revolutionized natural language processing by learning rich representations from massive and diverse text data. While pre-trained LLMs have been widely customized for a variety of text-related tasks, their potential in wireless communications remains largely untapped. In this paper, we propose a novel channel state information (CSI) feedback framework for massive multiple-input multiple-output (MIMO) systems that leverages pre-trained LLMs to bridge the gap between natural language processing and wireless communication. To adapt LLMs for the CSI feedback task, we design specialized pre-processing, embedding, and post-processing modules that convert CSI data into token representations compatible with LLMs. Moreover, to address the limitations of a single model and enhance performance in diverse environments and under various compression ratios, we introduce a mixture-of-agents (MoA)based framework that integrates the complementary strengths of multiple LLMs. Simulation results demonstrate that our approach achieves superior CSI reconstruction performance, robust generalization to unseen scenarios, and significant training cost reduction even with limited data. These findings confirm that the transferable knowledge acquired from large-scale natural language pre-training can effectively enhance CSI feedback, offering a promising direction for intelligent and efficient nextgeneration wireless communication systems.

Mathematical Problem Solving in Arabic: Assessing Large Language Models

Large Language Models (LLMs) such as GPT, LLaMA, and PaLM have transformed the field of Natural Language Processing (NLP) by achieving remarkable results in text generation, summarization, translation, question answering, and dialogue systems. Their wide adoption across industries highlights their usefulness but also exposes a critical limitation—hallucination. Hallucination occurs when models generate information that is false, misleading, or fabricated. These errors can vary from small factual mistakes, like incorrect dates or figures, to serious inaccuracies that may cause harm in sensitive areas such as healthcare, education, and software development. This paper explores the concept and classification of hallucinations in LLMs, examines techniques to reduce them—including prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG)—and discusses ethical implications and real-world applications. By comparing multiple strategies, the study aims to contribute to developing more reliable and trustworthy AI systems.

In the field of Natural Language Processing (NLP), the lack of support for minority languages, especially Uyghur, the scarcity of Uyghur language corpora in the agricultural domain, and the lightweight nature of large language models remain prominent issues. This study proposes a method for constructing a bilingual (Uyghur and Chinese) lightweight specialized large language model for the agricultural domain. By utilizing a mixed training approach of Uyghur and Chinese, we extracted Chinese corpus text from agricultural-themed books in PDF format using OCR (Optical Character Recognition) technology, converted the Chinese text corpus into a Uyghur corpus using a rapid translation API, and constructed a bilingual mixed vocabulary. We applied the parameterized Transformer model algorithm to train the model for the agricultural domain in both Chinese and Uyghur. Furthermore, we introduced a context detection and fail-safe mechanism for the generated text. The constructed model possesses the ability to support bilingual reasoning in Uyghur and Chinese in the agricultural domain, with higher accuracy and a smaller size that requires less hardware. It (our work) addresses issues such as the scarcity of Uyghur corpora in the agricultural domain, mixed word segmentation and word vector modeling in Uyghur for widespread agricultural languages, model lightweighting and deployment, and the fragmentation of non-relevant texts during knowledge extraction from small-scale corpora. The lightweight design of the model reduces hardware requirements, facilitating deployment in resource-constrained environments. This advancement promotes agricultural intelligence, aids in the development of specific applications and minority languages (such as agriculture and Uyghur), and contributes to rural revitalization.

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance.