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Using the chain rule: ∂k ∂σ = exp − d 2σ2 · ∂ ∂σ − d 2 σ−2

First Partial Derivative ∂k ∂σ Rewrite k= exp − d 2 σ−2

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

cs.CL · 2026-04-28 · unverdicted · novelty 5.0

G-Loss builds a document-similarity graph and uses semi-supervised label propagation to guide fine-tuning of language models, yielding higher accuracy than standard losses on five classification benchmarks.

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  • G-Loss: Graph-Guided Fine-Tuning of Language Models cs.CL · 2026-04-28 · unverdicted · none · ref 37

    G-Loss builds a document-similarity graph and uses semi-supervised label propagation to guide fine-tuning of language models, yielding higher accuracy than standard losses on five classification benchmarks.