  As the academic landscape expands, the challenge of efficiently identifying
potentially high-impact articles among the vast number of newly published works
becomes critical. This paper introduces a promising approach, leveraging the
capabilities of fine-tuned 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 discerns
the shared semantic features of highly impactful papers from a large collection
of title-abstract and potential impact pairs. These semantic features are
further utilized to regress an improved metric, TNCSI_SP, which has been
endowed with value, field, and time normalization properties. Additionally, a
comprehensive dataset has been constructed and released for fine-tuning the
LLM, containing over 12,000 entries with corresponding titles, abstracts, and
TNCSI_SP. The quantitative results, with 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 competitive counterparts. Finally,
we demonstrate a real-world application 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
assessing newborn article impact.
