Text_Classification
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Linguistically Informed Graph Model and Semantic Contrastive Learning for Korean Short Text Classification
2026-03-04 02:17:13JaeGeon Yoo, Byoungwook Kim, Yeongwook Yang, Hong-Jun JangAbstract
Short text classification (STC) remains a challenging task due to the scarcity of contextual information and labeled data. However, existing approaches have pre-dominantly focused on English because most benchmark datasets for the STC are primarily available in English. Consequently, existing methods seldom incorporate the linguistic and structural characteristics of Korean, such as its agglutinative morphology and flexible word order. To address these limitations, we propose LIGRAM, a hierarchical heterogeneous graph model for Korean short-text classification. The proposed model constructs sub-graphs at the morpheme, part-of-speech, and named-entity levels and hierarchically integrates them to compensate for the limited contextual information in short texts while precisely capturing the grammatical and semantic dependencies inherent in Korean. In addition, we apply Semantics-aware Contrastive Learning (SemCon) to reflect semantic similarity across documents, enabling the model to establish clearer decision boundaries even in short texts where class distinctions are often ambiguous. We evaluate LIGRAM on four Korean short-text datasets, where it consistently outperforms existing baseline models. These outcomes validate that integrating language-specific graph representations with SemCon provides an effective solution for short text classification in agglutinative languages such as Korean.
Abstract (translated)
短文本分类（STC）任务由于缺乏上下文信息和标注数据而仍然具有挑战性。然而，现有的方法主要集中在英语上，因为大多数基准数据集都是以英语为主的。因此，现有方法很少考虑韩语的语言和结构特征，例如其黏着形态和灵活的词序特点。为了解决这些问题，我们提出了LIGRAM模型，这是一种用于韩语短文本分类的层次异构图模型。该模型构建了音素级、词性级和命名实体级别的子图，并分层地整合它们以弥补短文本中上下文信息不足的问题，同时精确捕捉到韩语内在的语法和语义依存关系。 此外，我们还应用了基于语义感知对比学习（SemCon）的方法来反映文档之间的语义相似性，在短文本中即使类别区分模糊的情况下也能帮助模型建立更清晰的决策边界。我们在四个韩语短文本数据集上对LIGRAM进行了评估，结果表明它在所有情况下都优于现有的基线模型。这些成果验证了将特定语言图表示与SemCon相结合可以为像韩语这样的黏着语系提供有效的短文本分类解决方案。
URL
https://arxiv.org/abs/2603.03652
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OpenAutoNLU: Open Source AutoML Library for NLU
2026-03-02 12:56:54Grigory Arshinov, Aleksandr Boriskin, Sergey Senichev, Ayaz Zaripov, Daria Galimzianova, Daniil Karpov, Leonid SanochkinAbstract
OpenAutoNLU is an open-source automated machine learning library for natural language understanding (NLU) tasks, covering both text classification and named entity recognition (NER). Unlike existing solutions, we introduce data-aware training regime selection that requires no manual configuration from the user. The library also provides integrated data quality diagnostics, configurable out-of-distribution (OOD) detection, and large language model (LLM) features, all within a minimal lowcode API. The demo app is accessible here this https URL.
Abstract (translated)
OpenAutoNLU 是一个开源的自动化机器学习库，专门用于自然语言理解（NLU）任务，涵盖了文本分类和命名实体识别（NER）。与现有的解决方案不同，我们引入了数据感知的训练模式选择机制，无需用户进行手动配置。该库还提供集成的数据质量诊断、可配置的出站分布（OOD）检测以及大型语言模型（LLM）功能，并且所有这些都在一个极简的低代码API中实现。演示应用可以在这里访问：[此处应为URL]。 请注意，原始文本中的链接未被明确给出，实际使用时请确保正确插入有效的URL地址。
URL
https://arxiv.org/abs/2603.01824
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Assessing Crime Disclosure Patterns in a Large-Scale Cybercrime Forum
2026-03-02 08:59:32Raphael Hoheisel, Tom Meurs, Jai Wientjes, Marianne Junger, Abhishta Abhishta, Masarah Paquet-CloustonAbstract
Cybercrime forums play a central role in the cybercrime ecosystem, serving as hubs for the exchange of illicit goods, services, and knowledge. Previous studies have explored the market and social structures of these forums, but less is known about the behavioral dynamics of users, particularly regarding participants' disclosure of criminal activity. This study provides the first large-scale assessment of crime disclosure patterns in a major cybercrime forum, analysing over 3.5 million posts from nearly 300k users. Using a three-level classification scheme (benign, grey, and crime) and a scalable labelling pipeline powered by large language models (LLMs), we measure the level of crime disclosure present in initial posts, analyse how participants switch between levels, and assess how crime disclosure behavior relates to private communications. Our results show that crime disclosure is relatively normative: one quarter of initial posts include explicit crime-related content, and more than one third of users disclose criminal activity at least once in their initial posts. At the same time, most participants show restraint, with over two-thirds posting only benign or grey content and typically escalating disclosure gradually. Grey initial posts are particularly prominent, indicating that many users avoid overt statements and instead anchor their activity in ambiguous content. The study highlights the value of LLM-based text classification and Markov chain modelling for capturing crime disclosure patterns, offering insights for law enforcement efforts aimed at distinguishing benign, grey, and criminal content in cybercrime forums.
Abstract (translated)
网络犯罪论坛在网络犯罪生态系统中扮演着核心角色，作为非法商品、服务和知识交换的中心。先前的研究探讨了这些论坛的市场和社会结构，但对于用户的行为动态了解较少，特别是关于参与者披露犯罪活动的情况。本研究提供了对一个主要网络犯罪论坛内犯罪信息披露模式的首次大规模评估，分析了近30万用户的逾350万个帖子。通过采用三级分类体系（良性、灰色和犯罪）及由大型语言模型（LLM）驱动的可扩展标签流水线，我们测量了初始帖子中所包含的犯罪披露水平，分析了参与者在不同层级之间的切换，并评估了犯罪信息披露行为与私人通信的关系。 研究结果表明，犯罪信息披露是相对常规的现象：四分之一以上的初始帖子包含了明确的犯罪相关内容，超过三分之一的用户至少在其初始帖子中披露过一次犯罪活动。与此同时，大多数参与者表现出谨慎态度，三分之二以上的人仅发布良性或灰色内容，并通常逐步增加其披露的强度。灰色的初始帖子尤为突出，表明许多用户避免做出直接声明，而是将他们的行为锚定在模棱两可的内容上。 该研究强调了基于LLM的文本分类和马尔科夫链建模对于捕捉犯罪信息披露模式的价值，为旨在区分网络犯罪论坛内良性、灰色及犯罪内容的执法努力提供了见解。
URL
https://arxiv.org/abs/2603.01624
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A Human-Centered Workflow for Using Large Language Models in Content Analysis
2026-02-27 14:32:29Ivan ZupicAbstract
While many researchers use Large Language Models (LLMs) through chat-based access, their real potential lies in leveraging LLMs via application programming interfaces (APIs). This paper conceptualizes LLMs as universal text processing machines and presents a comprehensive workflow for employing LLMs in three qualitative and quantitative content analysis tasks: (1) annotation (an umbrella term for qualitative coding, labeling and text classification), (2) summarization, and (3) information extraction. The workflow is explicitly human-centered. Researchers design, supervise, and validate each stage of the LLM process to ensure rigor and transparency. Our approach synthesizes insights from extensive methodological literature across multiple disciplines: political science, sociology, computer science, psychology, and management. We outline validation procedures and best practices to address key limitations of LLMs, such as their black-box nature, prompt sensitivity, and tendency to hallucinate. To facilitate practical implementation, we provide supplementary materials, including a prompt library and Python code in Jupyter Notebook format, accompanied by detailed usage instructions.
Abstract (translated)
尽管许多研究者通过聊天界面使用大语言模型（LLMs），但其真正潜力在于通过应用程序编程接口（API）调用LLMs。本文提出将LLMs概念化为通用文本处理机器，并针对三类定性与定量内容分析任务——（1）标注（涵盖定性编码、标签与文本分类的统称）、（2）摘要生成、（3）信息抽取——提出了系统化的工作流程。该流程明确以人为中心：研究者主导设计、监督并验证LLM处理的每个阶段，以确保研究的严谨性与透明度。我们的方法综合了政治学、社会学、计算机科学、心理学及管理学等多学科的方法论文献，提出了针对LLMs关键局限性的验证方案与最佳实践，包括其黑箱特性、提示词敏感性及幻觉倾向。为便于实践应用，我们提供了补充材料，包含提示词库及Jupyter Notebook格式的Python代码，并附有详细使用说明。
URL
https://arxiv.org/abs/2603.19271
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Structured Prompt Optimization for Few-Shot Text Classification via Semantic Alignment in Latent Space
2026-02-27 07:31:16Jiasen Zheng, Zijun Zhou, Huajun Zhang, Junjiang Lin, Jingyun Jia, Qi WangAbstract
This study addresses the issues of semantic entanglement, unclear label structure, and insufficient feature representation in few-shot text classification, and proposes an optimization framework based on structured prompts to enhance semantic understanding and task adaptation under low-resource conditions. The framework first uses a pretrained language model to encode the input text and obtain basic semantic representations. It then introduces structured prompts composed of multi-dimensional semantic factors and integrates them with text features through a learnable combination mechanism, which forms task-related representations with clear boundaries in the latent space. To further strengthen the consistency between text representations and label semantics, the method constructs a structured label embedding matrix and employs a cross-space alignment mechanism to ensure stable matching between textual features and label attributes. In addition, the model applies prompt orthogonality constraints and a joint optimization objective to maintain independence across different semantic factors in the prompts, allowing the structured prompts to provide transparent and controllable guidance for classification decisions. Three types of sensitivity experiments, including learning rate sensitivity, prompt length sensitivity, and data scale sensitivity, are designed to evaluate the stability and robustness of the framework under different conditions. Experimental results show that the proposed structured prompt optimization framework effectively alleviates semantic conflicts and label ambiguity in few-shot text classification. It significantly improves performance on accuracy, precision, recall, and AUC, and demonstrates strong cross-task applicability.
Abstract (translated)
这项研究针对少量样本文本分类中的语义纠缠、标签结构不明确以及特征表示不足等问题，提出了一种基于结构化提示词的优化框架。该框架旨在提升在资源匮乏条件下的语义理解和任务适应能力。 具体来说，该框架首先利用预训练的语言模型对输入文本进行编码，并获取基础的语义表示。然后引入由多维语义因素组成的结构化提示词，并通过可学习组合机制将其与文本特征相融合，在潜在空间中形成具有明确边界的任务相关表示。为了进一步增强文本表征和标签语义之间的一致性，该方法构建了一个结构化的标签嵌入矩阵，并采用跨空间对齐机制来确保文本特征和标签属性之间的稳定匹配。 此外，模型还应用了提示词正交约束以及联合优化目标，以保持不同语义因素在提示中的独立性。这使得结构化提示能够为分类决策提供透明且可控的指导。研究设计了三种敏感性实验（学习率敏感性、提示长度敏感性和数据规模敏感性），用以评估该框架在不同条件下的稳定性和鲁棒性。 实验结果显示，所提出的基于结构化提示词的优化框架有效地缓解了少量样本文本分类中的语义冲突和标签模糊问题。它显著提高了准确率、精确度、召回率及AUC指标的表现，并展示了强大的跨任务适用性。
URL
https://arxiv.org/abs/2602.23753
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Asymmetric Idiosyncrasies in Multimodal Models
2026-02-26 08:16:47Muzi Tao, Chufan Shi, Huijuan Wang, Shengbang Tong, Xuezhe MaAbstract
In this work, we study idiosyncrasies in the caption models and their downstream impact on text-to-image models. We design a systematic analysis: given either a generated caption or the corresponding image, we train neural networks to predict the originating caption model. Our results show that text classification yields very high accuracy (99.70\%), indicating that captioning models embed distinctive stylistic signatures. In contrast, these signatures largely disappear in the generated images, with classification accuracy dropping to at most 50\% even for the state-of-the-art Flux model. To better understand this cross-modal discrepancy, we further analyze the data and find that the generated images fail to preserve key variations present in captions, such as differences in the level of detail, emphasis on color and texture, and the distribution of objects within a scene. Overall, our classification-based framework provides a novel methodology for quantifying both the stylistic idiosyncrasies of caption models and the prompt-following ability of text-to-image systems.
Abstract (translated)
在这项工作中，我们研究了描述模型（caption models）的特性及其对文本到图像生成模型的影响。设计了一套系统的分析方法：给定由某特定模型生成的文字说明或对应图片，训练神经网络预测其使用的原始描述模型。实验结果显示，基于文字分类的方法能够达到非常高的准确率（99.70%），表明不同的描述模型具有独特的风格特征。然而，这些独特标志在生成的图像中却几乎消失不见，即便对于最先进的Flux模型，通过图片进行分类的准确性也降至最多50%。 为了更好地理解跨模态差异的原因，我们进一步分析了数据集，发现生成的图片未能保留文字说明中的关键变化，例如细节程度、对颜色和纹理的关注以及场景中物体分布的不同。总体而言，我们的基于分类的方法为量化描述模型的独特风格特征及文本到图像系统遵循提示的能力提供了一种新颖且具有创新性的方法论。
URL
https://arxiv.org/abs/2602.22734
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Beyond the Star Rating: A Scalable Framework for Aspect-Based Sentiment Analysis Using LLMs and Text Classification
2026-02-24 16:45:17Vishal Patil, Shree Vaishnavi Bacha, Revanth Yamani, Yidan Sun, Mayank KejriwalAbstract
Customer-provided reviews have become an important source of information for business owners and other customers alike. However, effectively analyzing millions of unstructured reviews remains challenging. While large language models (LLMs) show promise for natural language understanding, their application to large-scale review analysis has been limited by computational costs and scalability concerns. This study proposes a hybrid approach that uses LLMs for aspect identification while employing classic machine-learning methods for sentiment classification at scale. Using ChatGPT to analyze sampled restaurant reviews, we identified key aspects of dining experiences and developed sentiment classifiers using human-labeled reviews, which we subsequently applied to 4.7 million reviews collected over 17 years from a major online platform. Regression analysis reveals that our machine-labeled aspects significantly explain variance in overall restaurant ratings across different aspects of dining experiences, cuisines, and geographical regions. Our findings demonstrate that combining LLMs with traditional machine learning approaches can effectively automate aspect-based sentiment analysis of large-scale customer feedback, suggesting a practical framework for both researchers and practitioners in the hospitality industry and potentially, other service sectors.
Abstract (translated)
客户提供的评论已经成为商家和其他顾客获取信息的重要来源。然而，有效地分析数以百万计的非结构化评论仍然是一项挑战。虽然大型语言模型（LLM）在自然语言理解方面显示出潜力，但其在大规模评价分析中的应用因计算成本和可扩展性问题而受到限制。本研究提出了一种混合方法，利用LLM进行方面识别，并采用传统的机器学习方法进行大规模情感分类。通过使用ChatGPT对采样的餐厅评论进行分析，我们确定了用餐体验的关键方面，并基于人工标注的评论开发了情感分类器，随后将其应用于17年间从主要在线平台收集到的470万条评论中。回归分析表明，我们的机器标注的方面在不同用餐体验、菜肴类型和地区方面显著解释了餐厅总体评分的变化。研究结果证明，结合LLM和传统机器学习方法可以有效地自动化大规模客户反馈的情感方面分析，这为酒店业研究人员和从业人员以及可能的服务行业提供了实用框架。
URL
https://arxiv.org/abs/2602.21082
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Explicit Grammar Semantic Feature Fusion for Robust Text Classification
2026-02-24 10:25:29Azrin Sultana, Firoz AhmedAbstract
Natural Language Processing enables computers to understand human language by analysing and classifying text efficiently with deep-level grammatical and semantic features. Existing models capture features by learning from large corpora with transformer models, which are computationally intensive and unsuitable for resource-constrained environments. Therefore, our proposed study incorporates comprehensive grammatical rules alongside semantic information to build a robust, lightweight classification model without resorting to full parameterised transformer models or heavy deep learning architectures. The novelty of our approach lies in its explicit encoding of sentence-level grammatical structure, including syntactic composition, phrase patterns, and complexity indicators, into a compact grammar vector, which is then fused with frozen contextual embeddings. These heterogeneous elements unified a single representation that captures both the structural and semantic characteristics of the text. Deep learning models such as Deep Belief Networks (DBNs), Long Short-Term Memory (LSTMs), BiLSTMs, and transformer-based BERT and XLNET were used to train and evaluate the model, with the number of epochs varied. Based on experimental results, the unified feature representation model captures both the semantic and structural properties of text, outperforming baseline models by 2%-15%, enabling more effective learning across heterogeneous domains. Unlike prior syntax-aware transformer models that inject grammatical structure through additional attention layers, tree encoders, or full fine-tuning, the proposed framework treats grammar as an explicit inductive bias rather than a learnable module, resulting in a very lightweight model that delivers better performance on edge devices
Abstract (translated)
自然语言处理（NLP）通过分析和分类文本中的深层次语法和语义特征，使计算机能够理解人类语言。现有模型利用大规模语料库训练的变压器模型来捕捉这些特征，但这种方法计算密集且不适合资源受限环境。因此，我们提出的研究结合了全面的语法规则与语义信息，构建了一个无需使用全参数化的变压器模型或重型深度学习架构的健壮、轻量级分类模型。 我们的方法新颖之处在于明确地将句子级别的语法结构（包括句法构成、短语模式和复杂度指标）编码为一个紧凑的语法规则向量，并将其与冻结的上下文嵌入相结合。这些异构元素统一在一个单一表示中，该表示同时捕捉文本的结构和语义特性。 我们使用深度信念网络（DBNs）、长短期记忆网络（LSTMs）、双向LSTMs以及基于变压器的BERT和XLNET等深度学习模型来训练和评估我们的模型，并通过改变训练轮数来进行实验。根据实验结果，统一特征表示模型能够捕捉文本的语义与结构属性，在各类基准模型上表现出2%-15%的优势，实现了异构领域中的更有效学习。 不同于之前的语法感知变换器模型（这些模型通过附加注意力层、树编码器或完全微调来注入语法结构），我们的框架将语法视为一种显式的归纳偏差而非可学习模块。这导致了一个非常轻量级的模型，在边缘设备上表现出更好的性能。
URL
https://arxiv.org/abs/2602.20749
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Natural Language Processing Models for Robust Document Categorization
2026-02-23 20:33:22Radoslaw Roszczyk, Pawel Tecza, Maciej Stodolski, Krzysztof SiwekAbstract
This article presents an evaluation of several machine learning methods applied to automated text classification, alongside the design of a demonstrative system for unbalanced document categorization and distribution. The study focuses on balancing classification accuracy with computational efficiency, a key consideration when integrating AI into real world automation pipelines. Three models of varying complexity were examined: a Naive Bayes classifier, a bidirectional LSTM network, and a fine tuned transformer based BERT model. The experiments reveal substantial differences in performance. BERT achieved the highest accuracy, consistently exceeding 99\%, but required significantly longer training times and greater computational resources. The BiLSTM model provided a strong compromise, reaching approximately 98.56\% accuracy while maintaining moderate training costs and offering robust contextual understanding. Naive Bayes proved to be the fastest to train, on the order of milliseconds, yet delivered the lowest accuracy, averaging around 94.5\%. Class imbalance influenced all methods, particularly in the recognition of minority categories. A fully functional demonstrative system was implemented to validate practical applicability, enabling automated routing of technical requests with throughput unattainable through manual processing. The study concludes that BiLSTM offers the most balanced solution for the examined scenario, while also outlining opportunities for future improvements and further exploration of transformer architectures.
Abstract (translated)
本文评估了几种机器学习方法在自动文本分类中的应用，并设计了一个用于不平衡文档分类和分布的演示系统。研究重点在于平衡分类准确性和计算效率，这是将AI集成到现实世界自动化流水线中时的关键考虑因素。研究人员考察了三种不同复杂度的模型：朴素贝叶斯分类器、双向LSTM网络以及经过微调的基于Transformer的BERT模型。实验结果显示性能存在显著差异。 BERT取得了最高的准确性，超过了99%，但需要更长的训练时间和更多的计算资源。BiLSTM模型则提供了一个强有力的折中方案，在保持适中的训练成本的同时达到了大约98.56%的准确率，并且具有较强的理解上下文的能力。相比之下，朴素贝叶斯分类器训练速度最快，仅需毫秒级时间，然而其准确性最低，平均在94.5%左右。 类别不平衡影响了所有方法，尤其在识别少数类别的表现上更加明显。为验证实际应用的可行性，实施了一个完全功能的演示系统，该系统能够自动处理技术请求，大大提高了处理效率并达到了手动处理无法企及的速度水平。研究结论认为BiLSTM模型是目前所考察场景中的最优方案，同时指出未来改进和进一步探索Transformer架构的机会。
URL
https://arxiv.org/abs/2602.20336
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PerSoMed: A Large-Scale Balanced Dataset for Persian Social Media Text Classification
2026-02-22 20:53:08Isun Chehreh, Ebrahim AnsariAbstract
This research introduces the first large-scale, well-balanced Persian social media text classification dataset, specifically designed to address the lack of comprehensive resources in this domain. The dataset comprises 36,000 posts across nine categories (Economic, Artistic, Sports, Political, Social, Health, Psychological, Historical, and Science & Technology), each containing 4,000 samples to ensure balanced class distribution. Data collection involved 60,000 raw posts from various Persian social media platforms, followed by rigorous preprocessing and hybrid annotation combining ChatGPT-based few-shot prompting with human verification. To mitigate class imbalance, we employed undersampling with semantic redundancy removal and advanced data augmentation strategies integrating lexical replacement and generative prompting. We benchmarked several models, including BiLSTM, XLM-RoBERTa (with LoRA and AdaLoRA adaptations), FaBERT, SBERT-based architectures, and the Persian-specific TookaBERT (Base and Large). Experimental results show that transformer-based models consistently outperform traditional neural networks, with TookaBERT-Large achieving the best performance (Precision: 0.9622, Recall: 0.9621, F1- score: 0.9621). Class-wise evaluation further confirms robust performance across all categories, though social and political texts exhibited slightly lower scores due to inherent ambiguity. This research presents a new high-quality dataset and provides comprehensive evaluations of cutting-edge models, establishing a solid foundation for further developments in Persian NLP, including trend analysis, social behavior modeling, and user classification. The dataset is publicly available to support future research endeavors.
Abstract (translated)
这项研究引入了第一个大规模、平衡良好的波斯语社交媒体文本分类数据集，旨在解决该领域内综合性资源不足的问题。该数据集包含九个类别（经济、艺术、体育、政治、社会、健康、心理、历史和科技）的共计36,000篇帖子，每个类别包括4,000份样本，确保了类别的分布均衡。数据收集阶段从各种波斯语社交媒体平台中获取了60,000条原始帖子，并随后进行了严格的预处理以及结合ChatGPT少样本提示和人工验证的混合标注过程。 为缓解类别不平衡的问题，我们采用了欠采样并移除语义冗余的方法，同时采用包括词汇替换和生成性提示在内的高级数据增强策略。我们在多个模型（包括BiLSTM、XLM-RoBERTa及其LoRA和AdaLoRA适应版本、FaBERT、基于SBERT的架构以及专为波斯语设计的TookaBERT基础版和大型版）上进行了基准测试。 实验结果显示，基于转换器的模型始终优于传统的神经网络模型，其中TookaBERT-Large表现最佳（精确率：0.9622，召回率：0.9621，F1分数：0.9621）。类别级别的评估进一步证实了所有类别的稳健性能，尽管社会和政治文本由于固有的模糊性而得分略低。 这项研究不仅提供了一个新的高质量数据集，还全面评测了前沿模型的性能，为波斯语自然语言处理（NLP）领域的未来发展奠定了坚实的基础，包括趋势分析、社交行为建模及用户分类等。该数据集对未来的科研工作开放，可供下载使用。
URL
https://arxiv.org/abs/2602.19333
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Evaluating Cross-Lingual Classification Approaches Enabling Topic Discovery for Multilingual Social Media Data
2026-02-19 03:46:11Deepak Uniyal, Md Abul Bashar, Richi NayakAbstract
Analysing multilingual social media discourse remains a major challenge in natural language processing, particularly when large-scale public debates span across diverse languages. This study investigates how different approaches for cross-lingual text classification can support reliable analysis of global conversations. Using hydrogen energy as a case study, we analyse a decade-long dataset of over nine million tweets in English, Japanese, Hindi, and Korean (2013--2022) for topic discovery. The online keyword-driven data collection results in a significant amount of irrelevant content. We explore four approaches to filter relevant content: (1) translating English annotated data into target languages for building language-specific models for each target language, (2) translating unlabelled data appearing from all languages into English for creating a single model based on English annotations, (3) applying English fine-tuned multilingual transformers directly to each target language data, and (4) a hybrid strategy that combines translated annotations with multilingual training. Each approach is evaluated for its ability to filter hydrogen-related tweets from noisy keyword-based collections. Subsequently, topic modeling is performed to extract dominant themes within the relevant subsets. The results highlight key trade-offs between translation and multilingual approaches, offering actionable insights into optimising cross-lingual pipelines for large-scale social media analysis.
Abstract (translated)
URL
https://arxiv.org/abs/2602.17051
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GATech at AbjadMed: Bidirectional Encoders vs. Causal Decoders: Insights from 82-Class Arabic Medical Classification
2026-02-17 16:13:08Ahmed Khaled KhamisAbstract
This paper presents system description for Arabic medical text classification across 82 distinct categories. Our primary architecture utilizes a fine-tuned AraBERTv2 encoder enhanced with a hybrid pooling strategies, combining attention and mean representations, and multi-sample dropout for robust regularization. We systematically benchmark this approach against a suite of multilingual and Arabic-specific encoders, as well as several large-scale causal decoders, including zero-shot re-ranking via Llama 3.3 70B and feature extraction from Qwen 3B hidden states. Our findings demonstrate that specialized bidirectional encoders significantly outperform causal decoders in capturing the precise semantic boundaries required for fine-grained medical text classification. We show that causal decoders, optimized for next-token prediction, produce sequence-biased embeddings that are less effective for categorization compared to the global context captured by bidirectional attention. Despite significant class imbalance and label noise identified within the training data, our results highlight the superior semantic compression of fine-tuned encoders for specialized Arabic NLP tasks. Final performance metrics on the test set, including Accuracy and Macro-F1, are reported and discussed.
Abstract (translated)
本文介绍了针对阿拉伯语医学文本分类的系统描述，涵盖了82个不同的类别。我们的主要架构使用了一种改进后的AraBERTv2编码器，该编码器融合了混合池化策略（结合注意力和平均表示）以及多样本 dropout 技术来增强鲁棒性正则化。我们通过一系列多语言和阿拉伯语特定的编码器，以及一些大规模因果解码器进行了系统的基准测试，包括零样本重排序方法Llama 3.3 70B及从Qwen 3B隐藏状态中提取特征的方法。我们的研究发现表明，在捕捉精细医学文本分类所需的精确语义边界方面，专业双向编码器显著优于因果解码器。 我们展示了优化于下一个标记预测的因果解码器会产生序列偏向性嵌入，这些嵌入在类别化任务中的效果不如双向注意力捕获的全局上下文。尽管训练数据中存在明显的类别不平衡和标签噪声问题，我们的结果强调了针对专业阿拉伯语自然语言处理（NLP）任务进行微调后的编码器所具有的卓越语义压缩能力。 最终，在测试集上报告并讨论了性能指标，包括准确率和宏F1值。
URL
https://arxiv.org/abs/2603.10008
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GATech at AbjadGenEval Shared Task: Multilingual Embeddings for Arabic Machine-Generated Text Classification
2026-02-17 16:05:08Ahmed Khaled KhamisAbstract
We present our approach to the AbjadGenEval shared task on detecting AI-generated Arabic text. We fine-tuned the multilingual E5-large encoder for binary classification, and we explored several pooling strategies to pool token representations, including weighted layer pooling, multi-head attention pooling, and gated fusion. Interestingly, none of these outperformed simple mean pooling, which achieved an F1 of 0.75 on the test set. We believe this is because complex pooling methods introduce additional parameters that need more data to train properly, whereas mean pooling offers a stable baseline that generalizes well even with limited examples. We also observe a clear pattern in the data: human-written texts tend to be significantly longer than machine-generated ones.
Abstract (translated)
我们介绍了针对AbjadGenEval任务检测AI生成的阿拉伯文本的方法。我们将多语言E5-large编码器进行了微调，以进行二分类，并探索了多种池化策略来合并token表示，包括加权层池化、多头注意力池化和门控融合。有趣的是，在测试集上，没有一种复杂方法的表现超过简单的均值池化，后者达到了0.75的F1分数。我们相信这是因为复杂的池化方法引入了额外需要大量数据进行适当训练的参数，而均值池法则提供了一个稳健的基础模型，即使在少量示例的情况下也能很好地泛化。此外，我们在数据中观察到一个明显的模式：人工撰写的文章往往比机器生成的文章要长得多。
URL
https://arxiv.org/abs/2603.10007
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Universal Algorithm-Implicit Learning
2026-02-16 14:05:07Stefano Woerner, Seong Joon Oh, Christian F. BaumgartnerAbstract
Current meta-learning methods are constrained to narrow task distributions with fixed feature and label spaces, limiting applicability. Moreover, the current meta-learning literature uses key terms like "universal" and "general-purpose" inconsistently and lacks precise definitions, hindering comparability. We introduce a theoretical framework for meta-learning which formally defines practical universality and introduces a distinction between algorithm-explicit and algorithm-implicit learning, providing a principled vocabulary for reasoning about universal meta-learning methods. Guided by this framework, we present TAIL, a transformer-based algorithm-implicit meta-learner that functions across tasks with varying domains, modalities, and label configurations. TAIL features three innovations over prior transformer-based meta-learners: random projections for cross-modal feature encoding, random injection label embeddings that extrapolate to larger label spaces, and efficient inline query processing. TAIL achieves state-of-the-art performance on standard few-shot benchmarks while generalizing to unseen domains. Unlike other meta-learning methods, it also generalizes to unseen modalities, solving text classification tasks despite training exclusively on images, handles tasks with up to 20$\times$ more classes than seen during training, and provides orders-of-magnitude computational savings over prior transformer-based approaches.
Abstract (translated)
URL
https://arxiv.org/abs/2602.14761
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Quantum-Inspired Self-Attention in a Large Language Model
2026-02-09 18:59:50Nikita Kuznetsov, Niyaz Ismagilov, Ernesto CamposAbstract
Recent advances in Natural Language Processing have been predominantly driven by transformer-based architectures, which rely heavily on self-attention mechanisms to model relationships between tokens in a sequence. Similarly, the field of Quantum Natural Language Processing, which seeks to leverage quantum principles to address challenges in language understanding and generation tasks, has seen the recent development of quantum self-attention mechanisms. We propose a classical quantum-inspired self-attention (QISA) mechanism and integrate it into the full autoregressive language modeling pipeline of GPT-1. To the best of our knowledge, this is the first integration of this kind, as previous quantum self-attention mechanisms have been primarily tested on text classification. In our experiments, QISA achieves better performance when compared to standard self-attention on the metrics character error rate ($15.5\times$ better), word error rate ($4.7 \times $) and cross-entropy loss ($13 \times$). This is achieved while only requiring a $ 2.6\times$ longer inference time.
Abstract (translated)
最近的自然语言处理（NLP）进展主要由基于变压器架构的技术推动，这些技术依赖于自注意力机制来建模序列中词汇单元之间的关系。同样，在量子自然语言处理领域——旨在利用量子原理解决语言理解和生成任务中的挑战——人们近期开发出了量子自注意力机制。我们提出了一种受量子启发的经典自注意力（QISA）机制，并将其集成到了GPT-1的完整自回归语言建模管道中。据我们所知，这是此类集成的首次尝试，因为之前的量子自注意力机制主要在文本分类任务上进行了测试。在我们的实验中，QISA相较于标准自注意力机制，在字符错误率（低15.5倍）、单词错误率（低4.7倍）和交叉熵损失（低13倍）这些指标上取得了更好的表现。同时，这仅需要2.6倍的推理时间。 翻译为中文如下： 最近在自然语言处理方面的进展主要由基于变换器架构的技术推动，这些技术依赖于自注意力机制来建模序列中词汇单元之间的关系。同样，在量子自然语言处理领域——旨在利用量子原理解决语言理解和生成任务中的挑战——人们近期开发出了量子自注意力机制。我们提出了一种受量子启发的经典自注意力（QISA）机制，并将其集成到了GPT-1的完整自回归语言建模管道中。据我们所知，这是首次此类集成，因为之前的量子自注意力机制主要在文本分类任务上进行了测试。在我们的实验中，与标准自注意力相比，QISA在字符错误率（降低15.5倍）、单词错误率（降低4.7倍）和交叉熵损失（降低13倍）这些指标上取得了更好的表现，并且只需要增加2.6倍的推理时间。
URL
https://arxiv.org/abs/2603.03318
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How Should We Model the Probability of a Language?
2026-02-09 17:46:56Rasul Dent, Pedro Ortiz Suarez, Thibault Cl\'erice, Beno\^it SagotAbstract
Of the over 7,000 languages spoken in the world, commercial language identification (LID) systems only reliably identify a few hundred in written form. Research-grade systems extend this coverage under certain circumstances, but for most languages coverage remains patchy or nonexistent. This position paper argues that this situation is largely self-imposed. In particular, it arises from a persistent framing of LID as decontextualized text classification, which obscures the central role of prior probability estimation and is reinforced by institutional incentives that favor global, fixed-prior models. We argue that improving coverage for tail languages requires rethinking LID as a routing problem and developing principled ways to incorporate environmental cues that make languages locally plausible.
Abstract (translated)
在全球超过7000种语言中，商用语言识别（LID）系统仅能可靠地识别几百种语言的书面形式。研究级别的系统在特定情况下可以扩大这一覆盖范围，但对于大多数语言而言，覆盖率仍然支离破碎或根本不存在。本文认为这种情况主要是自我限制的结果。特别是，它源于将LID视为脱离上下文的文本分类这种持久的观点，这掩盖了先验概率估计的核心作用，并且受到倾向于全球性、固定先验模型的制度激励所强化。我们认为，要提高尾部语言（即使用较少的语言）的覆盖率，需要重新审视LID作为一个路由问题的本质，并开发出有原则的方法来整合环境线索，使各种语言在局部环境中变得合理可信。
URL
https://arxiv.org/abs/2602.08951
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TATRA: Training-Free Instance-Adaptive Prompting Through Rephrasing and Aggregation
2026-02-06 19:20:35Bartosz Dziuba, Kacper Kuchta, Pawe{\l} Batorski, Przemys{\l}aw Spurek, Paul SwobodaAbstract
Large Language Models (LLMs) have improved substantially alignment, yet their behavior remains highly sensitive to prompt phrasing. This brittleness has motivated automated prompt engineering, but most existing methods (i) require a task-specific training set, (ii) rely on expensive iterative optimization to produce a single dataset-level prompt, and (iii) must be rerun from scratch for each new task. We introduce TATRA, a dataset-free prompting method that constructs instance-specific few-shot prompts by synthesizing on-the-fly examples to accompany a user-provided instruction. TATRA requires no labeled training data and avoids task-specific optimization loops, while retaining the benefits of demonstration-based prompting. Across standard text classification benchmarks, TATRA matches or improves over strong prompt-optimization baselines that depend on training data and extensive search. On mathematical reasoning benchmarks, TATRA achieves state-of-the-art performance on GSM8K and DeepMath, outperforming methods that explicitly optimize prompts on those tasks. Our results suggest that per-instance construction of effective in-context examples is more important than running long, expensive optimization loops to produce a single prompt per task. We will make all code publicly available upon acceptance of the paper. Code is available at this https URL
Abstract (translated)
大型语言模型（LLMs）在对齐方面有了显著的改进，但它们的行为仍然高度依赖于提示语句的表述。这种脆弱性促使了自动化提示工程的发展，然而大多数现有的方法需要特定任务的训练集、依靠昂贵的迭代优化来生成单一的数据级提示，并且每次遇到新的任务时都需要从头开始重新运行。 我们引入了一种名为TATRA的方法，这是一种不依赖于数据集的提示技术，它通过实时合成示例以伴随用户提供的指令来构建针对每个实例的少量样本提示。TATRA不需要标注的训练数据并且避免了特定任务的优化循环，同时保留了基于演示的提示方法的优点。 在标准文本分类基准测试中，与依赖于训练数据和大量搜索的强大提示优化基线相比，TATRA能够达到或超越它们的表现水平。在数学推理基准上，TATRA在GSM8K和DeepMath任务上实现了最先进的性能，超过了那些专门在这类任务上进行提示优化的方法。 我们的研究结果表明，针对每个实例构建有效的上下文示例比为单一的任务运行漫长且昂贵的优化循环更为重要。一旦论文被接受，我们将公开所有代码。代码可以在提供的链接处获取（假设原文中的链接可以正常使用）。
URL
https://arxiv.org/abs/2603.03298
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Cost-Aware Model Selection for Text Classification: Multi-Objective Trade-offs Between Fine-Tuned Encoders and LLM Prompting in Production
2026-02-06 03:54:28Alberto Andres Valdes GonzalezAbstract
Large language models (LLMs) such as GPT-4o and Claude Sonnet 4.5 have demonstrated strong capabilities in open-ended reasoning and generative language tasks, leading to their widespread adoption across a broad range of NLP applications. However, for structured text classification problems with fixed label spaces, model selection is often driven by predictive performance alone, overlooking operational constraints encountered in production systems. In this work, we present a systematic comparison of two contrasting paradigms for text classification: zero- and few-shot prompt-based large language models, and fully fine-tuned encoder-only architectures. We evaluate these approaches across four canonical benchmarks (IMDB, SST-2, AG News, and DBPedia), measuring predictive quality (macro F1), inference latency, and monetary cost. We frame model evaluation as a multi-objective decision problem and analyze trade-offs using Pareto frontier projections and a parameterized utility function reflecting different deployment regimes. Our results show that fine-tuned encoder-based models from the BERT family achieve competitive, and often superior, classification performance while operating at one to two orders of magnitude lower cost and latency compared to zero- and few-shot LLM prompting. Overall, our findings suggest that indiscriminate use of large language models for standard text classification workloads can lead to suboptimal system-level outcomes. Instead, fine-tuned encoders emerge as robust and efficient components for structured NLP pipelines, while LLMs are better positioned as complementary elements within hybrid architectures. We release all code, datasets, and evaluation protocols to support reproducibility and cost-aware NLP system design.
Abstract (translated)
大型语言模型（LLMs）如GPT-4o和Claude Sonnet 4.5在开放性推理和生成语言任务中展现了强大的能力，因此这些模型被广泛应用于各种自然语言处理（NLP）应用。然而，在结构化的文本分类问题中，这些问题具有固定的标签空间时，模型的选择往往只关注预测性能，而忽略了生产系统中的操作限制。在这项工作中，我们提出了两种截然不同的文本分类方法的系统比较：零样本和少量样本文本提示的大语言模型与完全微调过的编码器架构。我们在四个标准基准（IMDB、SST-2、AG News 和 DBPedia）上评估了这些方法，并测量了预测质量（宏F1分数）、推理延迟以及经济成本。我们将模型评估框架化为一个多目标决策问题，并通过帕累托前沿投影和反映不同部署方案的参数化效用函数来分析权衡。我们的结果表明，基于BERT家族的完全微调编码器模型在分类性能上具有竞争力甚至更优，同时其运行成本和延迟仅为零样本或少量样本文本提示的大语言模型的一到两个数量级。 总体而言，我们的研究发现认为，在标准文本分类工作负载中无差别地使用大型语言模型会导致次优的系统级结果。相反，微调过的编码器作为结构化NLP管道中的稳健且高效的组件而出现，而LLM则更适合在混合架构中作为补充元素。我们发布了所有代码、数据集和评估协议以支持可重复性以及成本意识型NLP系统的构建。
URL
https://arxiv.org/abs/2602.06370
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Robust Pre-Training of Medical Vision-and-Language Models with Domain-Invariant Multi-Modal Masked Reconstruction
2026-02-06 01:20:56Melika Filvantorkaman, Mohsen PiriAbstract
Medical vision-language models show strong potential for joint reasoning over medical images and clinical text, but their performance often degrades under domain shift caused by variations in imaging devices, acquisition protocols, and reporting styles. Existing multi-modal pre-training methods largely overlook robustness, treating it as a downstream adaptation problem. In this work, we propose Robust Multi-Modal Masked Reconstruction (Robust-MMR), a self-supervised pre-training framework that explicitly incorporates robustness objectives into masked vision-language learning. Robust-MMR integrates asymmetric perturbation-aware masking, domain-consistency regularization, and modality-resilience constraints to encourage domain-invariant representations. We evaluate Robust-MMR on multiple medical vision-language benchmarks, including medical visual question answering (VQA-RAD, SLAKE, VQA-2019), cross-domain image-text classification (MELINDA), and robust image-caption retrieval (ROCO). Robust-MMR achieves 78.9% cross-domain accuracy on VQA-RAD, outperforming the strongest baseline by 3.8 percentage points, and reaches 74.6% and 77.0% accuracy on SLAKE and VQA-2019, respectively. Under perturbed evaluation, Robust-MMR improves VQA-RAD accuracy from 69.1% to 75.6%. For image-text classification, cross-domain MELINDA accuracy increases from 70.3% to 75.2%, while retrieval experiments show a reduction in mean rank degradation from over 16 to 4.1 under perturbation. Qualitative results further demonstrate improved clinical reasoning for disease detection and structural abnormality assessment. These findings show that explicitly modeling robustness during pre-training leads to more reliable and transferable medical vision-language representations for real-world deployment.
Abstract (translated)
医学视觉-语言模型在对医疗图像和临床文本进行联合推理方面展示了强大的潜力，但其性能往往因成像设备、采集协议和报告风格的差异而产生的领域变化（domain shift）而下降。现有的多模态预训练方法很大程度上忽视了鲁棒性问题，并将其视为下游适应任务。在此项研究中，我们提出了Robust Multi-Modal Masked Reconstruction (Robust-MMR)，这是一种自监督预训练框架，它明确地将鲁棒性的目标纳入到了遮蔽视觉-语言学习之中。Robust-MMR集成了不对称扰动感知的屏蔽、领域一致性正则化和模态韧性约束以鼓励领域的不变表示。我们对多个医学视觉-语言基准进行了评估，包括医疗视觉问答（VQA-RAD, SLAKE, VQA-2019）、跨域图像文本分类（MELINDA）以及鲁棒的图像标题检索（ROCO）。在VQA-RAD上，Robust-MMR实现了78.9%的跨领域准确率，比最强基线高出了3.8个百分点，并分别达到了SLAKE和VQA-2019上的74.6%和77.0%准确率。在扰动评估下，Robust-MMR将VQA-RAD的准确性从69.1%提高到了75.6%。对于图像文本分类，在跨领域的MELINDA上精度从70.3%提升到75.2%，而在检索实验中显示了均位序退化率在扰动下的减少，从超过16降至4.1。定性结果进一步展示了改进的临床推理能力，用于疾病检测和结构异常评估。这些发现表明，在预训练期间显式地建模鲁棒性可以为真实世界的部署生成更可靠且可转移的医学视觉-语言表示。
URL
https://arxiv.org/abs/2602.17689
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Revisiting Prompt Sensitivity in Large Language Models for Text Classification: The Role of Prompt Underspecification
2026-02-04 07:59:28Branislav Pecher, Michal Spiegel, Robert Belanec, Jan CeginAbstract
Large language models (LLMs) are widely used as zero-shot and few-shot classifiers, where task behaviour is largely controlled through prompting. A growing number of works have observed that LLMs are sensitive to prompt variations, with small changes leading to large changes in performance. However, in many cases, the investigation of sensitivity is performed using underspecified prompts that provide minimal task instructions and weakly constrain the model's output space. In this work, we argue that a significant portion of the observed prompt sensitivity can be attributed to prompt underspecification. We systematically study and compare the sensitivity of underspecified prompts and prompts that provide specific instructions. Utilising performance analysis, logit analysis, and linear probing, we find that underspecified prompts exhibit higher performance variance and lower logit values for relevant tokens, while instruction-prompts suffer less from such problems. However, linear probing analysis suggests that the effects of prompt underspecification have only a marginal impact on the internal LLM representations, instead emerging in the final layers. Overall, our findings highlight the need for more rigour when investigating and mitigating prompt sensitivity.
Abstract (translated)
大型语言模型（LLMs）被广泛用作零样本和少量样本分类器，其任务行为主要通过提示来控制。越来越多的研究发现，LLM 对提示的变化非常敏感，即使是微小的改变也可能导致性能上的大幅波动。然而，在许多情况下，对这种敏感性的调查是使用提供最少任务说明且弱化了模型输出空间约束的不明确提示来进行的。在本工作中，我们认为观察到的大部分提示敏感性可以归因于提示的不明确性。我们系统地研究并比较了不明确提示与提供具体指令的提示之间的敏感度差异。通过性能分析、对数几率值分析以及线性探测，我们发现不明确提示表现出更高的性能波动和更低的相关词令牌的对数值，而指令提示则较少受到这些问题的影响。然而，线性探测分析表明，提示不明确性的效果只在模型内部表示的最终层中产生边际影响，而不是影响整体表示结构。总体而言，我们的研究结果强调了在调查和缓解提示敏感性时需要更加严谨的态度。
URL
https://arxiv.org/abs/2602.04297
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