2024/08/07 by Penghai Zhao, Qinghua Xing, Zhao, Penghai +12 · 4 citations
Biochemistry, Genetics and Molecular Biology · Decision Sciences · #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #Data Quality and Management #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2408.03934
openalex publication_date 2024/08/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As the academic landscape expands, the challenge of efficiently identifying impactful newly published articles grows increasingly vital. This paper introduces a promising approach, leveraging the capabilities of LLMs to predict the future impact of newborn articles solely based on titles and abstracts. Moving beyond traditional methods heavily reliant on external information, the proposed method employs LLM to discern the shared semantic features of highly impactful papers from a large collection of title-abstract pairs. These semantic features are further utilized to predict the proposed indicator, TNCSISP, which incorporates favorable normalization properties across value, field, and time. To facilitate parameter-efficient fine-tuning of the LLM, we have also meticulously curated a dataset containing over 12,000 entries, each annotated with titles, abstracts, and their corresponding TNCSISP values. The quantitative results, with an MAE of 0.216 and an NDCG@20 of 0.901, demonstrate that the proposed approach achieves state-of-the-art performance in predicting the impact of newborn articles when compared to several promising methods. Finally, we present a real-world application example for predicting the impact of newborn journal articles to demonstrate its noteworthy practical value. Overall, our findings challenge existing paradigms and propose a shift towards a more content-focused prediction of academic impact, offering new insights for article impact prediction.