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arXiv Machine Learning By Michal Podstawski

Big Claims, Small Margins: Do Reported GraphLLM Gains Survive Simple Baselines?

Read the original on arXiv Machine Learning →

The paper evaluates whether recent LLM-based node classification methods on text‑attributed graphs truly outperform simple baselines. By tuning four baseline models on the same data splits and comparing them to the best reported LLM results, the authors find that while some gains are modest (e.g., Correct & Smooth trailing by 2.3–2.8 percentage points), several reported improvements remain significant. They also highlight evaluation risks such as test‑set hyperparameter tuning, misuse of test‑pool labels, undisclosed baseline reuse, and inconsistencies across papers.

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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