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

arXiv:2604.25853v3 Announce Type: replace 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. We present G-Loss, a graph-guided loss function that incorporates semi-supervised label propagation to use structural relationships within the embedding manifold. G-Loss builds a document-similarity graph that captures global semantic relationships, thereby guiding the model to

Source: arXiv cs.CL — read the full report at the original publisher.

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