Interpreting Transformer-Based Classifiers via Clustering

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.

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.

Text classification is a fundamental task in natural language processing (NLP). Deep-learning-based text classification methods usually have two stages: training and inference. However, the training dataset is only used in the training stage. To make full use of the training dataset in the inference stage in order to improve model performance, we propose a k-nearest neighbors retrieval augmented method (KRA) for deep-learning-based text classification models. KRA works by first constructing a storage system that stores the embeddings of the training samples during the training stage. During the inference stage, the model retrieves the top k-nearest neighbors of the testing text from the storage. Then, we use text augmentation methods to expand the retrieved neighbors, including traditional augmentation methods and a large language model (LLM)-based method. Next, the method weights the augmented neighbors based on their distances from the target text and incorporates their labels into the inference of the final results accordingly. We evaluate our KRA method on six benchmark datasets using four commonly used deep learning models: CNN, LSTM, BERT, and RoBERTa. The results demonstrate that KRA significantly improves the classification performance of these models, with an average accuracy improvement of 0.3% for BERT and up to 0.4% for RoBERTa. These improvements highlight the effectiveness and generalizability of KRA across different models and datasets, making it a valuable enhancement for a wide range of text classification tasks.

Extracting information from unstructured and semi-structured geoscience literature is a crucial step in conducting geological research. The traditional machine learning extraction paradigm requires a substantial amount of high-quality manually annotated data for model training, which is time-consuming, labor-intensive, and not easily transferable to new fields. Recently, large language models (LLMs) (e.g., ChatGPT, GPT-4, and LLaMA), have shown great performance in various natural language processing (NLP) tasks, such as question answering, machine translation, and text generation. A substantial body of work has demonstrated that LLMs possess strong in-context learning (ICL) and even zero-shot learning capabilities to solve downstream tasks without specifically designed supervised fine-tuning. In this paper, we propose utilizing LLMs for geoscience literature information extraction. Specifically, we design a hierarchical PDF parsing pipeline and an automated knowledge extraction process, which can significantly reduce the need for manual data annotation, assisting geoscientists in literature data mining. For the hierarchical PDF parsing pipeline, firstly, a document layout detection model fine-tuned on geoscience literature is employed for layout detection, obtaining layout detection information for the document. Secondly, based on the document layout information, an optical character content parsing model is used for content parsing, obtaining the text structure and plain text corresponding to the content. Finally, the text structure and plain text are combined and reconstructed to ultimately obtain the parsed structured data. For the automated knowledge extraction process, firstly, the parsed long text is segmented into paragraphs to adapt to the input length limit of LLMs. Subsequently, a few-shot prompting method is employed for structured knowledge extraction, encompassing two tasks: attribute value extraction and triplet extraction. In attribute value extraction, prompts are generated automatically by the LLMs based on the subdomain and attribute names, facilitating the location and extraction of values related to subdomain attribute names in the text. For triplet extraction, the LLMs employ a procedural approach to entity extraction, entity type extraction, and relation extraction, following the knowledge graph structure pattern. Finally, the extracted structured knowledge is stored in the form of knowledge graphs, facilitating further analysis and integration of various types of knowledge from the literature. Our proposed approach turns out to be simple, flexible, and highly effective in geoscience literature information extraction. Demonstrations of information extraction in subdomains such as radiolarian fossils and fluvial facies have yielded satisfactory results. The extraction efficiency has significantly improved, and feedback from domain experts indicates a relatively high level of accuracy in the extraction process. The extracted results can be used to construct a foundational knowledge graph for geoscience literature, supporting the comprehensive construction and efficient application of a geoscience knowledge graph.

Work orders, equipment information, technical records and best practices documents contain within them a wealth of insights related to Equipment Maintenance which can be unlocked with Natural Language Processing tasks like classification, clustering, named entity recognition or part of speech tagging. But obtaining large enough labelled data sets in Equipment Maintenance domain manually is prohibitive and very expensive. This lack of labeled Equipment Maintenance data can be overcome with Large Language Models (LLMs) such as GPT-3, BERT that are pretrained transformer networks, considered state-of-the-art when it comes to Natural Language Processing (NLP) tasks. However, the vocabulary understood by these LLMs are mostly from English Language and need to be fine-tuned to understand industry and organization specific vocabulary and acronyms. This paper explores the potential of a domain specific LLM model for oil and gas industries. Data that are of good quality and that provide a comprehensive overview of industry are collected. This corpus of text contains documents like work orders, equipment data and technical documents. A custom tokenizer is trained on this data to identify domain specific terminology. A comparative study is done with other off-the-shelf tokenizers: BERT and RoBERTa, to compare the effectiveness of the tokenization. With millions of work orders and equipment documents, training pipelines had to parallelized so that training can occur on multiple GPUs. A comprehensive study of multiple training methods is done in this paper. Model and tokenizer developed were packaged and archived to be consumed in machine learning pipelines to specific use-cases across the organization. For an organization adopting digital transformation, the availability of an organization specific LLM is an enabler to extract insights from millions of documents containing free text. The applicability of such models spans across multiple disciplines like Maintenance, Reliability, Safety etc. and streamlines the development of highly accurate and robust text analytics.

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.

Plagiarism, the unauthorized reuse of text, fueled by the ease of access to online content, is a pressing concern for academia, publishers, and authors. Paraphrasing, a common tactic in textual plagiarism, compounds the problem further. The automatic detection of paraphrased plagiarism in text documents is a fundamental task in Natural Language Processing (NLP), crucial for maintaining academic integrity and authenticity. This paper presents an extensive investigation into Urdu sentential paraphrased plagiarism detection leveraging advanced Deep Neural Networks (DNNs) and Large Language Models (LLMs). The study builds upon the foundational work and proposes modifications to the Deep Text Reuse and Paraphrased Plagiarism Detection (D-TRaPPD) architecture to incorporate state-of-the-art pre-trained LLMs. The proposed approach, SELLM-D-TRaPPD, integrates various language models, including contextualized sentence embedding-based LLMs, language-agnostic and multilingual transformer-based LLMs, and multilingual knowledge-distilled transformer-based LLMs. We evaluated these models against three benchmark Urdu sentential paraphrase corpora—Urdu Sentential Paraphrase Corpus, Urdu Short Text Reuse Corpus, and Semi-automatic Urdu Sentential Paraphrase Corpus. The results demonstrate the effectiveness of SELLM-D-TRaPPD with LLMs, achieving F1 scores of 92.09%, 96.70%, and 98.23%, respectively. A comparative analysis with existing state-of-the-art methods shows significant performance improvements, establishing SELLM-D-TRaPPD as the new leading approach for Urdu sentential paraphrased plagiarism detection. These findings highlight the value of leveraging advanced neural network architectures and pre-trained LLMs in improving the accuracy and effectiveness of paraphrased plagiarism detection in Urdu, addressing a crucial gap in Urdu NLP research.

With the rapid development of large language models (LLMs) in natural language processing and generation tasks, researchers are continuously validating their application potential in the field of medical artificial intelligence (AI). Generative Pre-trained Transformer 5 (GPT-5), Pathways Language Model 2 (PaLM-2), Large Language Model Meta AI 3 (Llama-3), Gemini-2.5, and various medical vertical models have demonstrated excellent performance in many medical and health-related tasks. However, LLMs still face key bottlenecks in real-world medical scenarios, including “illusions” slow knowledge updates, and a lack of interpretability. These problems severely restrict the safe deployment of large language models in high-risk medical scenarios and have become a major obstacle to the transition of medical artificial intelligence from research to clinical application. Retrieval-augmented generation (RAG) technology has therefore become an important solution for improving the credibility of medical LLMs. RAG significantly reduces the risk of information errors by retrieving authoritative medical knowledge before generation while maintaining knowledge updates without retraining. A review of research progress on RAG in the medical field is of great theoretical value and practical significance for promoting the design, evaluation, and standardized application of trustworthy medical AI. This review aims to systematically summarize the research progress of RAG in medical scenarios, including the technical frameworks, typical applications, and development trends of three types of methods—naive RAG, advanced RAG, and modular RAG—and further discuss their significance in medical reliability, health equity, and personalized medicine. The naive RAG architecture, through a standard “index-retrieval-generation” process, transforms external knowledge bases into a structured vector space that can be queried by an LLM, achieving positive results in electronic medical record (EMR) summary, preoperative assessment, and medical question answering (QA). However, naive RAG still has limitations in retrieval accuracy and cross-modal processing. To address these shortcomings, advanced RAG methods have been devised that significantly improve the model’s decision-making capabilities by improving retrieval strategies, enhancing inference chains, and introducing self-reflection mechanisms. Furthermore, modular RAG design offers composable, multi-module systems to support multi-source knowledge integration and complex task decomposition. In terms of application value, RAG’s contributions to the healthcare field are primarily reflected in these aspects. On the one hand, it significantly improves the reliability of medical AI by introducing traceable knowledge to reduce illusions. On the other hand, it helps promote healthcare equity by driving a shift toward a patient-centered healthcare model through localized knowledge bases and multilingual support. However, the deployment of RAG in medical scenarios still faces many challenges, such as data privacy risks. Future research should focus on improving self-supervised reflection capabilities and developing cross-modal knowledge fusion technologies. As research develops, RAG is expected to become a core foundational capability of future intelligent healthcare systems, further promoting the development of safe and efficient intelligent healthcare.

BackgroundDisease name recognition is a fundamental task in clinical natural language processing, enabling the extraction of critical patient information from electronic health records. While recent advances in large language models (LLMs) have shown promise, most evaluations have focused on English, and little is known about their robustness in low-resource languages such as Japanese. In particular, whether these models can perform reliably on previously unseen in-hospital data, which differs from training data in writing styles and clinical contexts, has not been thoroughly investigated.ObjectiveThis study evaluated the robustness of fine-tuned LLMs for disease name recognition in Japanese clinical notes, with a particular focus on their performance on in-hospital data that was not included during training.MethodsWe used two corpora for this study: (1) a publicly available set of Japanese case reports denoted as CR, and (2) a newly constructed corpus of progress notes, denoted as PN, written by ten physicians to capture stylistic variations of in-hospital clinical notes. To reflect real-world deployment scenarios, we first fine-tuned models on CR. Specifically, we compared a LLM and a baseline-masked language model (MLM). These models were then evaluated under two conditions: (1) on CR, representing the in-domain (ID) setting with the same document type, similar to training, and (2) on PN, representing the out-of-domain (OOD) setting with a different document type. Robustness was assessed by calculating the performance gap (ie, the performance drop from in-domain to out-of-domain settings).ResultsThe LLM demonstrated greater robustness, with a smaller performance gap in F1-scores (ID–OOD = −8.6) compared to the MLM baseline performance (ID–OOD = −13.9). This indicated more stable performance across ID and OOD settings, highlighting the effectiveness of fine-tuned LLMs for reliable use in diverse clinical settings.ConclusionsFine-tuned LLMs demonstrate superior robustness for disease name recognition in Japanese clinical notes, with a smaller performance gap. These findings highlight the potential of LLMs as reliable tools for clinical natural language processing in low-resource language settings and support their deployment in real-world health care applications, where diversity in documentation is inevitable.

This chapter introduces essential systems and tools for leveraging various natural language processing (NLP) tasks and large language models (LLMs) in qualitative analysis, emphasizing accessibility for noncoders. It begins with an overview of the Python programming language and introduces tools such as the Pandas library, integrated development environments (IDEs), Jupyter notebooks, and application programming interfaces (APIs) for accessing LLMs. A second section provides a hands-on guide for setting up a computer environment, with step-by-step code examples designed to familiarize readers with NLP libraries and LLMs. It emphasizes the acquisition of programming skills to better adapt and utilize LLM capabilities for qualitative data analysis while providing multiple resources for further learning.

Large language models (LLMs) have achieved remarkable success in natural language processing tasks but still struggle with complex causal and logical reasoning. Previous neuro-symbolic methods can be summarized into a two-stage framework: first translating natural language (NL) problems into symbolic language (SL) representation, and then performing the symbolic reasoning process. To facilitate this direction, we provide a comprehensive survey, summarizing two main challenges including complex logical question-answering (QA) and cross-question logical consistency, and further propose a new taxonomy. To achieve precise symbolic representation and enhance the accuracy of LLMs’ logical reasoning, we propose several effective and efficient approaches, including adaptively selecting the most suitable SL for each QA problem, a data-driven approach to determine the fine-tuning samples order, and an efficient multi-agent debate framework with sparse communication. Our future research will focus on theoretical analysis for optimal SL selection, translation refinement and robust neuro-symbolic approach to improve LLMs' reasoning.

Long Short-Term Memory (LSTM) networks are unique to exercise data in its memory cell with long-term memory as Natural Language Processing (NLP) tasks have inklings of intensive time and computational power due to their complex structures like magnitude language model Transformer required to pre-train and learn billions of data performing different NLP tasks. In this paper, a dynamic chaotic model is proposed for the objective of transforming neurons states in network with neural dynamic characteristics by restructuring LSTM as Chaotic Neural Oscillatory-Long-Short Term Memory (CNO-LSTM), where neurons in LSTM memory cells are weighed in substitutes by oscillatory neurons to speed up computational training of language model and improve text classification accuracy for real-world applications. From the implementation perspective, five popular datasets of general text classification including binary, multi classification and multi-label classification are used to compare with mainstream baseline models on NLP tasks. Results showed that the performance of CNO-LSTM, a simplified model structure and oscillatory neurons state in exercising different types of text classification tasks are above baseline models in terms of evaluation index such as Accuracy, Precision, Recall and F1. The main contributions are time reduction and improved accuracy. It achieved approximately 46.76% of the highest reduction training time and 2.55% accuracy compared with vanilla LSTM model. Further, it achieved approximately 35.86% in time reduction compared with attention model without oscillatory indicating that the model restructure has reduced GPU dependency to improve training accuracy.

Natural Language Inference (NLI) is a fundamental task in Natural Language Processing (NLP).However, adapting NLI models to new domains remains challenging due to the high cost of collecting domain-specific training data.While prior work proposed 15 sentence transformation rules to automate training data generation, these rules do not sufficiently capture the diversity of natural language.We propose a novel framework that combines Out-of-Distribution (OOD) detection and BERT-based clustering to identify premise-hypothesis pairs in the SNLI dataset that are not covered by existing rules and discover four new transformation rules from them.Using these rules with Chain-of-Thought (CoT) prompting and Large Language Models (LLMs), we generate high-quality training data and augment the SNLI dataset.Our method yields consistent performance improvements across dataset sizes, achieving +0.85%p accuracy on 2k samples and +0.15%p on 550k samples.Furthermore, a distribution-aware augmentation strategy enhances performance across all scales.Beyond manual explanations, we extend our framework to automatically-generated explanations (CoT-Ex), demonstrating that they provide a scalable alternative to human-written explanations and enable reliable rule discovery.

Text Classification has been a well-studied task in Natural Language Processing. In this study deep learning methods are applied to train classifiers for the purpose of analyzing a data set of institutional change propaganda technique samples. Specifically this study explores how collective emotions underpin individuals’ participation in disruption of the hydropower institutionalized energy system. By exploring the context of local initiatives which seek to undermine the hydropower energy system, this study taps into micro-foundations of institutional disruption. The preliminary qualitative study reveals existing patterns in how emotions are tied to various steps of instigating and promoting institutional change. Text classification techniques are further applied to confirm and complement the preliminary findings. In particular Long short-term memory neural networks as well as transformer-based models are applied at two levels of classification: sentence level and token level. The trained classification models help investigate how generalizable the preliminary findings are. By combining qualitative and quantitative this study extends prior research by unpacking emotional dynamics implicated in institutional disruption by explaining how actors mobilize collective emotions as a basis to build support for institutional change.

Large language models (LLMs) demonstrate impressive capabilities across a range of natural language processing (NLP) tasks. However, they are highly sensitive to prompt design, which significantly affects their ability to align outputs with user intent. Poorly crafted prompts can result in misleading or irrelevant responses. Nevertheless, selecting the most effective prompt from several candidates remains an open challenge. Despite the growing importance of prompt engineering, there is no comprehensive framework to systematically evaluate prompts across multiple dimensions, such as similarity, performance, efficiency, and consistency, particularly in scenarios where performance can be traded off against computational cost or consistency. In this study, we propose a novel scoring framework to evaluate handcrafted prompts across four essential dimensions: Similarity, performance, efficiency (measured by latency, input tokens, and output tokens), and consistency. Considering Arabic, a relatively low-resource, morphologically rich language, as a case study, we evaluated this framework on six diverse text classification tasks: Dialect identification, sentiment analysis, offensive language detection, stance detection, emotion detection, and sarcasm detection. Our methodology assesses prompts across multiple LLMs (GPT-4o mini, LLaMA, ALLAM, and Claude 3.5 Haiku), providing valuable insights into model-specific and task-specific performance patterns. Results demonstrate that no single prompt universally excels across all dimensions; rather, optimal prompts vary based on specific task requirements and evaluation priorities. The proposed framework enables the identification of the most effective prompts for each application context while revealing important trade-offs between performance metrics. By addressing the unique challenges of Arabic NLP, this research not only advances prompt engineering for underrepresented languages but also provides a systematic and adaptable methodology for prompt evaluation that can enhance LLM performance across a range of linguistic contexts, diverse domains, tasks, and various model architectures.