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arXiv · 2505.08941

ForeCite: Adapting Pre-Trained Language Models to Predict Future Citation Rates of Academic Papers

Gavin Hull·Alex Bihlo
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Abstract

Predicting the future citation rates of academic papers is an important step toward the automation of research evaluation and the acceleration of scientific progress. We present $\textbf{ForeCite}$, a simple but powerful framework to append pre-trained causal language models with a linear head for average monthly citation rate prediction. Adapting transformers for regression tasks, ForeCite achieves a test correlation of $\rho = 0.826$ on a curated dataset of 900K+ biomedical papers published between 2000 and 2024, a 27-point improvement over the previous state-of-the-art. Comprehensive scaling-law analysis reveals consistent gains across model sizes and data volumes, while temporal holdout experiments confirm practical robustness. Gradient-based saliency heatmaps suggest a potentially undue reliance on titles and abstract texts. These results establish a new state-of-the-art in forecasting the long-term influence of academic research and lay the groundwork for the automated, high-fidelity evaluation of scientific contributions.

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Gavin Hull, Alex Bihlo. 2025-05-13. ForeCite: Adapting Pre-Trained Language Models to Predict Future Citation Rates of Academic Papers. https://arxiv.org/abs/2505.08941

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