arXiv AI June 16, 2026 By Aditya Sharma, Vinti Agarwal, Rajesh Kumar

G-Loss: Graph-Guided Fine-Tuning of Language Models

arXiv:2604. 25853v3 Announce Type: replace-cross Abstract: Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operate only within local neighborhoods and fail to account for the global semantic structure.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 28

TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer

arXiv:2601. 02366v3 Announce Type: replace-cross Abstract: Graph-based recommendation has achieved great success in recent years.

By Yiwen Chen, Yiqing Wu, Huishi Luo, Fuzhen Zhuang, Deqing Wang, Zhao Zhang

llms rag fine-tuning

Hugging Face Trending Papers

Jun 29

PromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph Learning

Text-Attributed Graphs (TAGs) combine textual semantics with graph structure and are central to many graph learning tasks. However, existing fusion methods often treat text and structure as separate inputs in a shallow, one-way pipeline, which limits deep interaction between modalities and weakens performance under sparse connectivity or cross-graph generalisation.

llms multimodal safety

arXiv AI

Jun 30

PromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph Learning

arXiv:2606. 30291v1 Announce Type: new Abstract: Text-Attributed Graphs (TAGs) combine textual semantics with graph structure and are central to many graph learning tasks.

By Zhifei Hu, Alexandra I. Cristea

llms multimodal safety

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

1d ago

Domain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation

arXiv:2608. 16269v1 Announce Type: cross Abstract: Recent advances in neural topic models with pre-trained language models (PLMs) have achieved strong performance by leveraging general-domain pre-training, yet their topic interpretability often degrades on specialized corpora.

By Seung-Won Seo, Won Ik Cho, Yongmin Yoo

llms rag fine-tuning benchmarks safety

arXiv AI

Jul 13

Integrating Large Language Models and Graph Convolutional Networks for Semi-Supervised Image Classification

arXiv:2607. 09104v1 Announce Type: cross Abstract: While the growing availability of image data has driven significant advances, labeling datasets remains costly and time-consuming.

By Camila Piscioneri Magalh\~aes, Lucas Pascotti Valem

llms computer-vision efficiency multimodal

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Semi-Supervised Text-Attributed Graph Distillation

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