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arXiv Machine Learning By Emma Ceccherini, Daniel Lawson, Anjulika Salhan

Where A Small Language Model Helps in Invoice Categorisation, Understood Through Embedding Geometry

Read the original on arXiv Machine Learning →

The paper investigates using a small language model (SBERT) for invoice categorisation, showing that fine‑tuning on a single GPU yields 0.96 accuracy and 0.9 F1 with about 100 client‑specific invoices. It analyses the embedding geometry, finding that the sentence‑embedding space is globally anisotropic but locally isotropic, with clusters strongly linked to vendor identity. The study demonstrates that an in‑house SLM can outperform zero‑shot LLMs and vendor baselines while improving cost, security, and interpretability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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