AINeutralarXiv – CS AI · 6h ago7/10
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On Semantic Loss Fine-Tuning Approach for Preventing Model Collapse in Causal Reasoning
Researchers demonstrate that standard fine-tuning of transformer models on causal reasoning tasks causes catastrophic collapse where models learn trivial solutions while appearing accurate. They propose a semantic loss function with graph-based constraints that prevents collapse and achieves stable, context-dependent causal reasoning with 42.7% improvement over baseline models.