14 citations · 17 across the 33 of their papers we have counts for
9 papers · 1 filter
Fine-Tuning Diffusion Language Models with Context Selection and Target Weighting
Loay Mualem, Lluís Pastor-Pérez, Vinh Tong +4
Supervised fine-tuning of discrete diffusion language models masks some response tokens and trains the model to recover their original values from the visible context. The masking…
Evolutionary Soups: Evolving Mixture-of-Experts for Multi-Objective LLM Alignment
Lingxiao Kong, Steffen Staab, Cong Yang +2
Large language models are increasingly required to generate responses that satisfy multiple competing objectives. Since optimal trade-offs depend on both user preferences and input…
Conformalized Large Language Models under Configuration Shift
Yuqicheng Zhu, Jialin Yu, Lin Li +7
Conformal prediction (CP) is a distribution-free framework for uncertainty quantification that has recently been adapted to large language models (LLMs), providing prediction sets…
GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models
Hongkuan Zhou, Tristan Rehm, Nadeem Nazer +3
Industrial inspection requires more than binary anomaly detection: a practical system should determine whether an anomaly exists, localize the defective region, identify the defect…
SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs
Prateek Chaturvedi, Yuqicheng Zhu, Hongkuan Zhou +6
Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) enables natural language interaction with structured enterprise knowledge, yet existing agentic approaches that perfor…
KAPPS: A knowledge-based CPPS Architecture for the Circular Factory
Etienne Hoffmann, Jan-Felix Klein, Sören Weindel +7
While linear manufacturing relies on homogeneous materials and predefined process sequences, circular manufacturing reintroduces used products with heterogeneous and uncertain cond…
Ex-GraphRAG: Interpretable Evidence Routing for Graph-Augmented LLMs
Yoav Kor Sade, Arvindh Arun, Rishi Puri +2
GraphRAG conditions language models on subgraphs retrieved from knowledge graphs, encoded via message-passing GNNs. Because these encoders entangle node contributions through itera…
Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction
Luu Huu Phuc, Ratan Bahadur Thapa, Mojtaba Nayyeri +3
We introduce Graph-Augmented Sequence-to-Sequence (GA-S2S), a novel framework that integrates a T5-small encoder-decoder with a Relational Graph Attention Network (RGAT) to improve…
Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks
Jingcheng Wu, Ratan Bahadur Thapa, Mojtaba Nayyeri +4
Relational databases store much of the world's structured information, and they are essential for driving complex predictive applications. However, deep learning progress on relati…