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cs.LG2025
GraphOmni: A Comprehensive and Extensible Benchmark Framework for Large Language Models on Graph-theoretic Tasks
Hao Xu, Xiangru Jian, Xinjian Zhao +9
This paper introduces GraphOmni, a comprehensive benchmark designed to evaluate the reasoning capabilities of LLMs on graph-theoretic tasks articulated in natural language. GraphOm…
cs.CL2025
MoSE: Hierarchical Self-Distillation Enhances Early Layer Embeddings
Andrea Gurioli, Federico Pennino, João Monteiro +1
Deploying language models often requires navigating accuracy vs. performance trade-offs to meet latency constraints while preserving utility. Traditional model distillation reduces…
cs.CL2025
Explain-Query-Test: Self-Evaluating LLMs Via Explanation and Comprehension Discrepancy
Saeid Asgari Taghanaki, Joao Monteiro
Large language models (LLMs) have demonstrated remarkable proficiency in generating detailed and coherent explanations of complex concepts. However, the extent to which these model…