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

arXiv:2604.25853 · cs.CL, cs.AI, cs.LG · Submitted 2026-04-28 · Read on arXiv

cs.CL, cs.AI, cs.LG

Submitted: 2026-04-28

Updated: 2026-08-27

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

Terminology

• Fine-Tuning — 70× in this paper · explained in 5 episodes
• Embeddings — 40× in this paper · explained in 2 episodes
• Loss Function — 20× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Benchmark — 17× in this paper · explained in 2 episodes
• Graph Construction — 10× in this paper · explained in 3 episodes
• Contrastive Learning — 8× in this paper · explained in 5 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Alignment — 7× in this paper · explained in 1 episode
• Consistency — 6× in this paper · explained in 3 episodes
• Cosine Similarity — 6× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Ablation study — 5× in this paper · explained in 1 episode
• Robustness — 5× in this paper · explained in 5 episodes
• Supervised Contrastive Loss — 5× in this paper
• Generalization — 4× in this paper · explained in 13 episodes
• Graph Convolutional Networks — 3× in this paper
• Representation Learning — 3× in this paper · explained in 2 episodes
• Semi-supervised Learning — 3× in this paper · explained in 4 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Adjacency Matrix — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Computational overhead — 2× in this paper · explained in 2 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• GNNs — 2× in this paper · explained in 2 episodes
• Label Propagation Algorithm — 2× in this paper
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Dynamic Adaptation — 1× in this paper · explained in 2 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Hybrid Models — 1× in this paper · explained in 2 episodes
• Movie Review — 1× in this paper
• Pseudo-labeling — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Label Propagation for Deep Semi-supervised Learning
• SimCSE: Simple Contrastive Learning of Sentence Embeddings
• Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
• DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

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