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A new study published in PLoS ONE reveals that scientists frequently reference The Beatles in academic papers, using a large language model to analyze over 3,200 instances across various disciplines.
A large-scale mapping of 11 million academic papers using semantic similarity and time-based slices, enabling analysis of research trends and connections.
This paper analyzes 80,814 papers from five top AI conferences (2017-2025) to show that major AI topics advance through abrupt 'topical phase transitions' rather than gradual growth. It proposes an early-warning signature for detecting such transitions and flags reasoning, agentic AI, and multimodal LLMs as topics to monitor through 2028.
This paper evaluates whether bibliometric structure improves LLM-assisted scientific literature synthesis by comparing six pipelines for generating cluster descriptions. Results show LLMs perform best in a hybrid workflow where bibliometric algorithms define clusters and LLMs generate readable descriptions.