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20232026
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cs.CL2026

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…

cs.CL2026

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…

cs.CL2025

SEMMA: A Semantic Aware Knowledge Graph Foundation Model

Arvindh Arun, Sumit Kumar, Mojtaba Nayyeri +4

Knowledge Graph Foundation Models (KGFMs) have shown promise in enabling zero-shot reasoning over unseen graphs by learning transferable patterns. However, most existing KGFMs rely…

cs.CL2025

From Tokens to Lattices: Emergent Lattice Structures in Language Models

Bo Xiong, Steffen Staab

Pretrained masked language models (MLMs) have demonstrated an impressive capability to comprehend and encode conceptual knowledge, revealing a lattice structure among concepts. Thi…

cs.CL2023

Robust Knowledge Extraction from Large Language Models using Social Choice Theory

Nico Potyka, Yuqicheng Zhu, Yunjie He +2

Large-language models (LLMs) can support a wide range of applications like conversational agents, creative writing or general query answering. However, they are ill-suited for quer…