Innovation
Methods, ideas, or system contributions that make the work stand out.
Atomic Contribution Claims
Scientific Drift
Large Language Models
Trend Analysis
Clustering
Drift Inspector: Exploring and Measuring Scientific Drift with Atomic Contribution Claims
🤖 AI Summary
This study addresses the challenge of quantifying disciplinary evolution in scientific abstracts, where contributions are often entangled with background information. To overcome the limitations of traditional keyword-based statistics, this work proposes the concept of decontextualized atomic contribution claims. Methodologically, large language models are employed to extract atomic contributions from 80,000 abstracts in the ACL Anthology, which are then integrated with text clustering to construct an interactive visualization map. This approach enables fine-grained, cross-year measurement of scientific drift that is traceable to specific papers, successfully revealing EMNLP’s trend migration toward multimodality and related directions. The findings are validated through manual verification, and the dataset is publicly released. Ultimately, this research establishes a novel paradigm for analyzing academic evolution.
📝 Abstract
Scientific abstracts mix contributions with background, motivation, and meta-language, so tools that read them as-is cannot separate what a field produces from what it discusses. We present Drift Inspector, an open-source system for measuring and exploring how a research field changes over time at the level of Atomic Contribution Claims (ACCs): decontextualized, contribution-bearing propositions an LLM extracts from each abstract before analysis. The system clusters these claims across years into an interactive map where every trend traces back to the claims and papers behind it. Applied to six years of EMNLP, it shows the field shifting away from classic NLP tasks toward LLM-era capabilities such as reasoning and multimodality -- a movement that keyword or whole-abstract counts blur. The released data extend beyond EMNLP: the same pipeline has processed the full ACL Anthology (346k claims, 80k abstracts, 423 venues). Extraction is human-validated and clustering checked against an external manually constructed taxonomy.
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