arXiv Machine Learning

Beyond Looking Up, Try Looking Around: Harmonizing Global Structure and Local Consistency in Optimal Transport for Short Text Clustering

arXiv:2607. 10548v1 Announce Type: cross Abstract: Pseudo-labeling based on Optimal Transport (OT) has become an effective mechanism for enhancing short text clustering.

arXiv Machine Learning

Jun 30

TextClusterLab: An Integrated Framework for Reliable Text Clustering Studies

arXiv:2606. 28328v1 Announce Type: cross Abstract: In recent years, text clustering has become a critical technique for applications including intent discovery, topic mining, and recommendation systems.

By Daoming Wan, Yizheng Huang, Jimmy X. Huang

llms rag benchmarks

arXiv AI

Jul 28

Flick: Few Labels Text Classification using K-Aware Intermediate Learning in Multi-Task Low-Resource Languages

arXiv:2506. 10292v2 Announce Type: replace-cross Abstract: Training deep learning networks with minimal supervision has gained significant research attention due to its potential to reduce reliance on extensive labelled data.

By Ali Almutairi, Abdullah Alsuhaibani, Shoaib Jameel, Aditya Joshi, Gelareh Mohammadi, Imran Razzak

llms nlp fine-tuning

arXiv Machine Learning

Aug 3

Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM

arXiv:2607. 28635v1 Announce Type: cross Abstract: In Natural Language Processing (NLP), dealing with underrepresented topics is challenging, especially in unsupervised tasks where clustering might not adequately capture minority topics.

By Noor Khalal, Abdallah Alaa-Eddine Djamai, Imed Keraghel, Mohamed Nadif

llms nlp safety

arXiv Machine Learning

Jul 21

LFM: Leveraging Foundation Models for Source-Free Universal Domain Adaptation

arXiv:2607. 17653v1 Announce Type: cross Abstract: Source-free universal domain adaptation (SF-UniDA) adapts a pre-trained source model to an unlabeled target domain under both covariate and label shifts, without access to source data.

By Jing Li, Pan Liu, Meng Zhao, Wanli Xue, Yanhong Yang, Xu Cheng, Fan Shi, Jianhua Zhang, Qinghua Hu, Shengyong Chen

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 24

Semantic-Aware Task Clustering for Constructive and Cooperative Multi-Tasking

arXiv:2607. 21426v1 Announce Type: new Abstract: Cooperative multi-task semantic communication (CMT-SemCom) improves task execution performance by leveraging shared representations.

By Ahmad Halimi Razlighi, Maximilian H. V. Tillmann, Edgar Beck, Bho Matthiesen, Armin Dekorsy

arXiv Machine Learning

Jun 2

Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs

arXiv:2602. 11641v2 Announce Type: replace Abstract: Text-attributed graphs (TAGs) associate nodes with textual attributes and graph structure, enabling GNNs to jointly model semantic and structural information.

By Yinlin Zhu, Di Wu, Xu Wang, Guocong Quan, Miao Hu

llms rag benchmarks safety

arXiv AI

Jul 3

HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures

arXiv:2607. 02266v1 Announce Type: cross Abstract: Most data-mixing methods assume the corpus has already been partitioned into groups, and the choice of those groups determines what a mixer can express.

By Ziyun Qiao, Yue Min, Ruining Chen, Yujun Li

rag efficiency

arXiv AI

Jun 2

NILC: Discovering New Intents with LLM-assisted Clustering

arXiv:2511. 05913v2 Announce Type: replace-cross Abstract: New intent discovery (NID) seeks to recognize both new and known intents from unlabeled user utterances, which finds prevalent use in practical dialogue systems.

By Hongtao Wang, Renchi Yang, Wenqing Lin

llms rag benchmarks

arXiv AI

Jun 4

ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation

arXiv:2509. 25289v4 Announce Type: replace-cross Abstract: Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue.

By Mohammadreza Bakhtyari, Bogdan Mazoure, Renato Cordeiro de Amorim, Guillaume Rabusseau, Vladimir Makarenkov

benchmarks

arXiv AI

Jul 24

Semi-Supervised Text-Attributed Graph Distillation

arXiv:2607. 20477v1 Announce Type: new Abstract: {\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph topology with rich textual semantics.

By Yurui Lai, Samir Moustafa, Renchi Yang, Tsz Nam Chan

llms efficiency benchmarks

arXiv Machine Learning

Jun 4

Contrastive Learning and Correlation Clustering for Sequences of Network Telescope Data

arXiv:2606. 04733v1 Announce Type: new Abstract: Understanding activities of Internet scanners is challenging; it often requires identifying relationships between sources, a task for which semantic annotations are scarce.

By Jannik Presberger, Alexander M\"annel, Maynard Koch, Thomas C. Schmidt, Matthias W\"ahlisch, Bjoern Andres

llms

arXiv Machine Learning

Jun 2

Robust Contrastive Graph Clustering with Adaptive Local-Global Integration

arXiv:2605. 28209v2 Announce Type: replace Abstract: Graph clustering is essential in graph analysis for revealing structural patterns and node communities.

By Lei Zhang, Fubo Sun, Haipeng Yang, Zhong Guan, Likang Wu

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