Text Classification

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12 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

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Latest papers 122

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Data-Efficient Adaptation of LLMs via Attention Head Reweighting
Jul 15, 2026Tuomas Oikarinen, Zixiao Chen, Charlotte Siska +3LLM Fine-Tuning Text Classification
UC San Diego, Microsoft Research · Microsoft Security AI · UC San Diego +1
A Durability and Cross-Language Transfer Benchmark for a Validated Teaching-Feedback Classification Protocol
Jul 13, 2026Esteban U. Vega BarajasMultilingual Language Model Evaluation Sentiment Analysis
Universidad de Guadalajara
UCSC NLP at SemEval-2026 Task 10: Boundary-Aware Span Extraction and RoBERTa Classification for Conspiracy Detection
Jul 6, 2026Dom Marhoefer, Milos Suvakovic, Glenn Grant-Richards +2Text Classification
UC Santa Cruz
Large-scale dataset of automatically classified rhetorical sections in scientific papers
Jul 3, 2026Daniel Verdi, Jacob Aarup Dalsgaard, Roberta SinatraDocument Information Extraction Text Classification
Graduate School of Education, Stanford University, United States · Center for Social Data Science (SODAS), University of Copenhagen, Denmark · Networks, Data, and Society (NERDS), IT University of Copenhagen, Denmark +2
Hybrid quantum-classical neural network for sentiment analysis
Jul 2, 2026Giacomo Cappiello, Filippo Caruso, Xing Liang +1Sentiment Analysis Hybrid Quantum-Classical ML
Center for Quantum Mathematics, University of Southern Denmark, Campusvej 55, Odense, 5230, Denmark. · Dept. of Physics and Astronomy, Florence Univ., via Sansone 1, I-50019 Sesto Fiorentino, Italy. · School of Computer Science and Mathematics, Kingston University London, KT1 2EE, United Kingdom.
Revising RVL-CDIP: Quantifying Errors and Test-Train Overlap
Jun 30, 2026Stefan Larson, Attila Nagy, Sam Desai +8Data Leakage Training Data Curation
Vanderbilt University · ML Collective · University of Michigan +1
Comparing BERT Sentence-Pair Classification and Few-Shot LLM Prompting for Detecting Threat and Solution Framing in German Climate News
Jun 25, 2026Raven Adam, David Maier, Marie KoglerText Classification Few-Shot Prompting
University of Graz, Austria · Technical University of Graz, Austria
AnySimLite: A Lightweight Few-Shot Similarity Encoder for On-Device Speech-Adjacent Classification
Jun 24, 2026Sourav Ghosh, Yash Bhatia, Keshav Goyal +3Semantic Textual Similarity Text Classification
Samsung R&D Institute Bangalore, India · ITBHU
Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model
Jun 24, 2026Abrar Alotaibi, Atta-ur Rahman, Raheel Alhaza +6Social Media Analysis Arabic NLP
Department of Computer Science (CS), College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 192, Dammam 31441, Saudi Arabia · Saudi Aramco Cybersecurity Chair, Department of Computer Science (CS), College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 192, Dammam 31441, Saudi Arabia
Text Distance from Nested and Hierarchical Repetitions: A Compression-Based Perspective
Jun 23, 2026Xiaojun Hu, Jing Wang, Jingwen Zhang +5Text Classification Sequence Modeling
Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, China. · International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, China. · School of Systems Science, Beijing Normal University, Beijing, China. +2
Learning Moral Diversity: Modelling Individual Perspectives in Moral Classification of Texts
Jun 22, 2026Yi Ren, Lewis Mitchell, Matthew RoughanAnnotator Disagreement Text Classification
School of Mathematical Sciences, Adelaide University
From Complaint Narratives to Monetary Relief: A Hybrid Machine Learning Framework for CFPB Consumer Complaints
Jun 21, 2026Zhuoer Wang, Sizhen Zhu, Xiongyu ChenText Classification
University of Illinois Urbana-Champaign · North Carolina State University · Carnegie Mellon University
Clusters are All You Need: Pre-Training the Tsetlin Machine with Semantic Clusters from Language Models for Interpretability
Jun 18, 2026Jiechao Gao, Rohan Kumar Yadav, Yuangang Li +4Interpretable ML Text Classification
Stanford University · Independent Researcher · University of California, Irvine +1
Distilling Examples into Task Instructions: Enhanced In-Context Learning for Real-World B2B Conversations
Jun 14, 2026Guy Rotman, Adi Kopilov, Danit Berger Zalmanson +1Text Classification Few-Shot Learning
A Context-Aware Dataset for Stance Detection in Bioethical Controversies on Reddit
Jun 11, 2026Hu Huang, Genan Dai, Fuqiang Niu +3Social Media Analysis Argument Mining
School of Cyber Science and Technology, University of Science and Technology of China, Hefei, China · School of Artificial Intelligence, Shenzhen Technology University, Shenzhen, China · School of Urban Planning and Design, Peking University, Shenzhen, China
MentalMARBERT: Domain-Adaptive Pre-training and Two-Stage Fine-Tuning for Arabic Mental Health Disorders Detection
Jun 10, 2026Fatimah Almalki, Areej Alhothali, Lulwah Alharigy +1Social Media Analysis Arabic NLP
Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah, Saudi Arabia · Department of Psychology College of Arts and Humanities King Abdulaziz University Jeddah, Saudi Arabia
A Resource for Enthymeme Detection in Controversial Political Discourse
Jun 10, 2026Martial Pastor, Nelleke OostdijkAnnotator Disagreement Human-in-the-Loop Annotation
Centre for Language Studies, Radboud University, Nijmegen
DECSELFMASK: Leveraging Unlabeled Text via Self-Relevance-Guided Masking for Decoder-Only Classification
Jun 8, 2026Pietro Ferrazzi, Matteo Merler, Giovanni Bonetta +2Self-Supervised Pre-Training Decoder-Only Language Models
Fondazione Bruno Kessler, Trento, Italy · University of Padova, Italy
Operationalizing Linguistic Methods through Prompt-Engineering Skills: An Automatic Chinese Web Neologism Detection Pipeline
Jun 7, 2026Yufeng Wu, Meichun LiuText Classification Prompt Engineering
City University of Hong Kong
Supervision versus Demonstration-Based In-Context Learning for Multiword Expression Classification
Jun 5, 2026Sercan Karakaş, Yusuf ŞimşekLLM Evaluation Zero-Shot Text Classification
University of Chicago · Fırat University
Adversarial Creation and Detection of AI-Generated Social Bot Content
Jun 5, 2026Mykola Trokhymovych, Ricardo Baeza-Yates, Alessandro Flammini +2AI-Generated Text Detection Text Classification
Universitat Pompeu Fabra, Barcelona, Spain · Observatory on Social Media, Indiana University, Bloomington, Indiana, USA · KTH Royal Institute of Technology, Stockholm, Sweden
Style or Content? Evaluating Style Classifiers with Controlled Content Overlap
Jun 5, 2026Zhuo Liu, Haozheng Du, Xiangxiang Xu +1Text Classification Classification
University of Rochester
CHALIS: A Challenge Dataset for Language Identification in Difficult Scenarios
Jun 4, 2026Michal Tichý, Jindřich LibovickýDistribution Shift Robustness Language Identification
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics, V Holešovičkách 747/2, 180 00 Praha, Czechia
Long Live Fine-Tuning: Task-Specific Transformers Outperform Zero-Shot LLMs for Misinformation Response Classification on Reddit
Jun 2, 2026JooYoung Lee, Lin Tian, Angela Brillantes +2LLM Evaluation Zero-Shot Text Classification
University of Technology Sydney, Sydney, Australia.
Cross-Prompt Generalization in Detecting AI-Generated Fake News Using Interpretable Linguistic Features
Jun 2, 2026Aya Vera-Jimenez, Samuel Jaeger, Calvin Ibenye +1Fake News Detection AI-Generated Text Detection
Department of Mathematics Kennesaw State University Marietta, Georgia, USA · School of Data Science and Analytics Kennesaw State University Marietta, Georgia, USA · Department of Computer Science Kennesaw State University Marietta, Georgia, USA
AutoTail-BSFGM: Class-Balance-Aware Fine-Tuning for Chinese Scholarly Text Classification
Jun 2, 2026Anling Xiang, Yuwen Yang, Yang ShenClass-Imbalanced Learning Long-Tailed Classification
Department of Intelligent Communication, School of Journalism and Communication, Minzu University of China, Beijing, China · ZeeLin (Beijing) Technology Co., Ltd., Beijing, China · School of Journalism and Communication, Tsinghua University, Beijing, China +1
Lingo_Research_Group at SemEval-2026 Task 9: Evaluating Prompt Variants for Polarization Detection
Jun 2, 2026Pritam Kadasi, Anuj Tiwari, Mayank SinghPrompt Sensitivity Text Classification
Lingo Research Group, Indian Institute of Technology Gandhinagar · Noida Institute of Engineering and Technology 2ML Collective
Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling
Jun 1, 2026Vladimir BeskorovainyiHuman-in-the-Loop Annotation Text Classification
Besk Tech · Moscow Institute of Physics and Technology (MIPT)
Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
May 27, 2026Tianyang Zhou, Wenbo Chen, Pierre Jinghong Liang +1LLM Interpretability Text Classification
1Carnegie Mellon University · 2Amazon
Keyphrase Generative Representation of Youth Crisis Conversations Beyond Static Taxonomies
May 26, 2026Abeer Badawi, Will Aitken, Lydia Sequeira +3Emotional Support Conversation Text Classification
York University, Canada · Vector Institute, Canada · Electrical and Computer Engineering, Queen’s University, Canada +1

Jul 15, 2026, cs.LGJ/K move · Enter open · S save

Data-Efficient Adaptation of LLMs via Attention Head Reweighting

Tuomas Oikarinen, Zixiao Chen, Charlotte Siska, Tsui-Wei Weng+2

UC San Diego, Microsoft Research · Microsoft Security AI · UC San Diego+1

LLM Fine-Tuning Text Classification Transformer Attention Parameter-Efficient Fine-Tuning Data-Efficient Learning

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Learning effectively from limited data is critical in domains like security where labeled examples are scarce. Large language models (LLMs) have demonstrated some capabilities for data-efficient learning, especially through parameter-efficient adaptation methods, but continue to struggle when faced with few samples for difficult tasks. To meet this challenge, we propose Attention Head Reweighting (AHR), a data-efficient method that adapts LLMs to new text-classification tasks by learning only a single scalar per attention head. This drastically reduces the number of parameters that need to be learned by making use of the functional specialization of individual attention heads. Experiments on diverse open-source text classification datasets show that AHR can outperform standard baselines like LoRA when learning from limited samples, despite having 200-1000x fewer trainable parameters, as our AHR only modifies ~0.0001% of the model's parameters. In addition, our learned weights are easy to interpret and can be analyzed to better understand the mechanisms and attention heads responsible for in-context learning abilities in LLMs.

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Latest papers 122