Improving Narrative Coherence in Dense Video Captioning through Transformer and Large Language Models

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

The extensive spread of harmful content, including hate speech, harassment, violent and adult material across online platforms and media channels poses significant challenges and has raised considerable concern within various societal groups. Government bodies, educators, and parents frequently find themselves in disagreement with media providers over the best methods to regulate and restrict such content. Automated content moderation technologies have become critical tools in addressing these challenges, particularly through natural language processing (NLP) techniques that can automatically detect and filter sensitive textual content, such as offensive language, violence, and adult materials, enabling platforms to enforce moderation policies on a large scale. Despite their widespread use, current moderation technologies face challenges related to detection accuracy, often resulting in substantial false positives and false negatives. To enhance content moderation systems, more advanced algorithms capable of accurately interpreting textual context are necessary. In this study, we assess current large language model (LLM)-based moderation solutions, specifically OpenAI moderation model and Llama-Guard-3, examining their effectiveness in detecting sensitive content. Additionally, we investigate the capabilities of contemporary LLMs, including OpenAI generative pre-trained transformer (GPT), Google Gemini, Meta Llama, Anthropic Claude, and small language models (SLMs) such as Google Gemma, in recognizing inappropriate content from diverse media sources. We also studied the performance of these models under adversarial attacks such as input perturbation and prompt injection. Our evaluation and comparative analysis utilize various textual datasets, such as X (Twitter) posts, Amazon product reviews, and news articles. The findings indicate that LLM-based approaches significantly demonstrate high accuracy and low rates of false positives and negatives. They also indicate the robustness of the models under various adversarial attacks. These results underscore the considerable potential for integrating advanced LLMs into websites and social media platforms, thereby enhancing content regulation and moderation effectiveness.

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

Multi-objective evolutionary neural architecture search for medical image analysis using transformer and large language models in advancing public health

Quantitative Models with Qualitative Scenarios: Simulation with Transformer and Large Language Models

This study uses various models to address network traffic classification, categorizing traffic into web, browsing, IPSec, backup, and email. We collected a comprehensive dataset from Arbor Edge Defender (AED) devices, comprising of 30,959 observations and 19 features. Multiple models were evaluated, including Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, XGBoost, Deep Neural Networks (DNN), Transformer, and two Large Language Models (LLMs) including GPT-4o and Gemini with zero- and few-shot learning. Transformer and XGBoost showed the best performance, achieving the highest accuracy of 98.95 and 97.56%, respectively. GPT-4o and Gemini showed promising results with few-shot learning, improving accuracy significantly from initial zero-shot performance. While Gemini Few-Shot and GPT-4o Few-Shot performed well in categories like Web and Email, misclassifications occurred in more complex categories like IPSec and Backup. The study highlights the importance of model selection, fine-tuning, and the balance between training data size and model complexity for achieving reliable classification results.

Code generation has largely improved development efficiency in the era of large language models (LLMs). With the ability to follow instructions, current LLMs can be prompted to generate code solutions given detailed descriptions in natural language. Many research efforts are being devoted to improving the correctness of LLM-generated code, and many benchmarks are proposed to evaluate the correctness comprehensively. Despite the focus on correctness, the time efficiency of LLM-generated code solutions is under-explored. Current correctness benchmarks are not suitable for time efficiency evaluation since their test cases cannot well distinguish the time efficiency of different code solutions. Besides, the current execution time measurement is not stable and comprehensive, threatening the validity of the time efficiency evaluation. To address the challenges in the time efficiency evaluation of code generation, we propose COFFE, a code generation benchmark for evaluating the time efficiency of LLM-generated code solutions. COFFE contains 398 and 358 problems for function-level and file-level code generation, respectively. To improve the distinguishability, we design a novel stressful test case generation approach with contracts and two new formats of test cases to improve the accuracy of generation. For the time evaluation metric, we propose efficienct@k based on CPU instruction count to ensure a stable and solid comparison between different solutions. We evaluate 14 popular LLMs on COFFE and identify four findings. Based on the findings, we draw some implications for LLM researchers and software practitioners to facilitate future research and usage of LLMs in code generation.

In recent years, the field of natural language processing has witnessed remarkable advancements due to the success of large language models. These models leverage the Transformer architecture and pre-training techniques to achieve impressive results. In this paper, we draw inspiration from large language models and apply these techniques into the task of named entity recognition in the domain of power grids, which is critical for building power grid knowledge graphs and question-answering systems. Specifically, we propose a BERT-CNN-BIGRU-CRF deep learning model for named entity recognition. This model effectively harnesses the semantic modeling capabilities and pre-training knowledge of BERT, which is based on the Transformer architecture. By incorporating CNN and BIGRU, the model captures and models both local and global features, respectively. The CRF layer is employed for label classification. This combination of components ensures a high level of recognition accuracy. To evaluate the performance of the proposed model, we train our model on annotated maintenance plan data. We compare its results with those of other commonly used models. The evaluation metrics include recall, precision, and F1 score, which are widely employed in named entity recognition tasks. Our proposed model achieves optimal performance across all three metrics, demonstrating its superiority over other models.

Researchers face a critical choice: how to use -- or not use -- large language models in their work. Using them well requires understanding the mechanisms that shape what LLMs can and cannot do. This chapter makes LLMs comprehensible without requiring technical expertise, breaking down six essential components: pre-training data, tokenization and embeddings, transformer architecture, probabilistic generation, alignment, and agentic capabilities. Each component is analyzed through both technical foundations and research implications, identifying specific affordances and limitations. Rather than prescriptive guidance, the chapter develops a framework for reasoning critically about whether and how LLMs fit specific research needs, finally illustrated through an extended case study on simulating social media dynamics with LLM-based agents.

Although traditional statistical techniques and machine learning methods have contributed significantly to genetics and, in particular, inherited disease diagnosis, they often struggle with complex, high-dimensional data, a challenge now addressed by state-of-the-art deep learning models. Large language models (LLMs), based on transformer architectures, have excelled in tasks requiring contextual comprehension of unstructured medical data. This systematic review examines the role of generative Artificial Intelligence (AI) methods in human medical genomics, focusing on the genetic research and diagnostics of both rare and common diseases. Automated keyword-based search in PubMed, bioRxiv, medRxiv, and arXiv was conducted, targeting studies on LLM applications in diagnostics and education within genetics and removing irrelevant or outdated models. A total of 195 studies were analyzed, highlighting the prospects of their applications in knowledge navigation, analysis of clinical and genetic data, and interaction with patients and medical professionals. Key findings indicate that while transformer-based models perform well across a diverse range of tasks (such as identification of tentative molecular diagnosis from clinical data or genetic variant interpretation), major challenges persist in integrating multimodal data (genomic sequences, imaging, and clinical records) into unified and clinically robust pipelines, facing limitations in generalizability and practical implementation in clinical settings. This review provides a comprehensive classification and assessment of the current capabilities and limitations of LLMs in transforming hereditary disease diagnostics and supporting genetic education, serving as a guide to navigate this rapidly evolving field, while outlining application use cases, implementation guidance, and forward-looking research directions.

Explainable Artificial Intelligence (XAI) constitutes a foundational pillar of responsible AI deployment, addressing the opacity of data-driven decision systems by making their internal logic transparent, auditable, and contestable. This chapter provides a systematic account of explainability and interpretability across the full spectrum of AI models — from classical supervised learning algorithms such as decision trees, support vector machines, and gradient-boosted ensembles to contemporary Large Language Models (LLMs) built on transformer architectures. Beginning with a structured taxonomy of XAI properties — scope, stage, model-dependency, and output type — the chapter critically examines canonical explanation methods including SHAP, LIME, attention visualization, mechanistic circuit analysis, and counterfactual generation.

Large Language Models (LLMs), built on Transformer architectures, exhibit remarkable generalization across a wide range of tasks. However, fine-tuning these models for specific tasks remains resource-intensive due to their extensive parameterization. In this paper, we explore two remarkable phenomena related to the attention mechanism during the fine-tuning of LLMs (where Wq, Wk, and Wv denote the weights of the query, key, and value layers, respectively). The first phenomenon, termed “Unequal Importance of Attention Matrices”, highlights the impact of fine-tuning different weight matrices. It shows that optimizing the Wv matrix yields significantly better performance than optimizing the Wk matrix. Fine-tuning only the Wq and Wv matrices is computationally efficient while delivering results comparable to, or even better than fine-tuning all three matrices (Wq, Wk, and Wv). The second phenomenon, “Attention Matrices with Customized Learning Rate Lead to Better Convergence”, emphasizes the importance of assigning distinct learning rates to these matrices. Specifically, a higher learning rate for the Wv matrix compared to Wq and Wk accelerates convergence and improves performance. Building on these insights, we propose a new strategy that improves fine-tuning efficiency in terms of both storage and time. Experimental results on benchmark datasets validate the effectiveness of this approach, supporting our theoretical findings. Our analysis lays the theoretical groundwork for configuring and improving algorithms in LLMs fine-tuning.

Empowered by Large Language Models (LLMs), substantial progress has been made in enhancing the EDA design flow in terms of high-level synthesis, such as direct translation from high-level language into RTL description. On the other hand, little research has been done for logic synthesis on the netlist generation. A direct application of LLMs for netlist generation presents additional challenges due to the scarcity of netlist-specific data, the need for tailored fine-tuning, and effective generation methods. This work first presents a novel training set and two evaluation sets catered for direct netlist generation LLMs, and an effective dataset construction pipeline to construct these datasets. Then this work proposes LLM4Netlist, a novel step-based netlist generation framework via fine-tuned LLM. The framework consists of a step-based prompt construction module, a fine-tuned LLM, a code confidence estimator, and a feedback loop module, and is able to generate netlist codes directly from natural language functional descriptions. We evaluate the efficacy of our approach with our novel evaluation datasets. The experimental results demonstrate that, compared to the average score of the 10 commercial LLMs listed in our experiments, our method shows a functional correctness increase of 183.41% on the NetlistEval dataset and a 91.07% increase on NGen. The training and testing data, along with the processing code, can be found at https://github.com/klyebit/LLM4Netlist.git.

Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving cancer treatment outcomes and safety. This study examined machine learning (ML) models to identify cancer patients at risk of HF using electronic health records (EHRs), including traditional ML, Time-Aware long short-term memory (T-LSTM), and large language models (LLMs) using novel narrative features derived from the structured medical codes. We identified a cancer cohort of 12,806 patients from the University of Florida Health, diagnosed with lung, breast, and colorectal cancers, among which 1,602 individuals developed HF after cancer. The LLM, GatorTron-3.9B, achieved the best F1 scores, outperforming the traditional support vector machines by 39%, the T-LSTM deep learning model by 7%, and a widely used transformer model, BERT, by 5.6%. The analysis shows that the proposed narrative features remarkably increased feature density and improved performance.

Inspired by the tremendous success of Large Language Models (LLMs), existing Radiology report generation methods attempt to leverage large models to achieve better performance. They usually adopt a Transformer to extract the visual features of a given X-ray image, and then, feed them into the LLM for text generation. How to extract more effective information for the LLMs to help them improve final results is an urgent problem that needs to be solved. Additionally, the use of visual Transformer models also brings high computational complexity. To address these issues, this paper proposes a novel context-guided efficient radiology report generation framework. Specifically, we introduce the Mamba as the vision backbone with linear complexity, and the performance obtained is comparable to that of the strong Transformer model. More importantly, we perform context retrieval from the training set for samples within each mini-batch during the training phase, utilizing both positively and negatively related samples to enhance feature representation and discriminative learning. Subsequently, we feed the vision tokens, context information, and prompt statements to invoke the LLM for generating high-quality medical reports. Extensive experiments on three X-ray report generation datasets (i.e., IU X-Ray, MIMIC-CXR, CheXpert Plus) fully validated the effectiveness of our proposed model. The source code is available at https://github.com/Event-AHU/Medical_ Image_Analysis.