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

Aditya Sharma, VINTI AGARWAL, Rajesh Kumar

Keywords: Graph-based loss, Language model fine-tuning, Label propagation(LPA), Semi-supervised learning(SSL), Text classification

Abstract: Traditional loss functions, including cross-entropy, contrastive, triplet, and supervised 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 learn more discriminative and robust embeddings. We evaluate G-Loss on five benchmark datasets covering key downstream classification tasks: MR (sentiment analysis), R8 and R52 (topic categorization), Ohsumed (medical document classification), and 20NG (news categorization). In the majority of experimental setups, G-Loss converges faster and produces semantically coherent embedding spaces, resulting in higher classification accuracy than models fine-tuned with traditional loss functions.

Submission Type: Full paper proceedings track submission (max 9 main pages).

Software: https://github.com/saditya13/G-Loss-LoG

Submission Number: 146