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School of Management, Anhui University, Hefei, 230601, China
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The rise of pre-trained language models has not only reshaped NLP innovation but also intensified the knowledge demands on researchers, with implications for future research directions.
More than half of algorithm mentions in NLP papers are for direct use, signaling a significant shift in how researchers engage with algorithms over time.
Research papers in Chinese LIS are becoming more novel, with collaboration patterns revealing that solo authorship is linked to lower novelty.
An optimal number of thought leaders can enhance team impact, but too many may stifle innovative ideas.
Classic algorithms maintain their dominance in influence, but their decline reveals a predictable loss of network centrality that could inform future research trajectories.
Results-based novelty alone beats combined theoretical, methodological, and results-based novelty when it comes to scientific impact, challenging the assumption that more novelty is always better.
