LLM4Impact: Integrating Heterogeneous Information for Scientific Impact Prediction

Key points

• Predicting the future impact of a newly published paper is challenging because it must be inferred from heterogeneous evidence available at publication time.1
• LLM4Impact combines semantic, graph, LLM, and temporal representations, and injects graph information into a frozen LLM through continuous prefix tokens.1
• A context aware gating mechanism adaptively weights different evidence, while a separate calibration module accounts for domain and temporal variation in citation scales.1
• We further construct a large-scale benchmark dataset with 2 million papers, leakage-safe point-in-time heterogeneous ego graphs, temporal splits, and both year-level and month-level citation targets.1

Sources (1)

• [1]LLM4Impact: Integrating Heterogeneous Information for Scientific Impact Prediction
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 02:18 PM

  In this paper, we present LLM4Impact, an evidence-aware method for scientific impact prediction that learns to represent, integrate, and calibrate heterogeneous information.

  Predicting the future impact of a newly published paper is challenging because it must be inferred from heterogeneous evidence available at publication time.

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