arXiv:2603.26791v3 Announce Type: replace-cross Abstract: Assessing a cited paper's impact is typically done by analyzing its citation context in isolation within the citing paper. While this focuses on the most directly relevant text, it prevents relative comparisons across all the works a paper cites. We propose Crystal, which instead jointly ranks all cited papers within a citing paper using large language models (LLMs). To mitigate LLMs' positional bias, we rank each list three times in a randomized order and aggregate the impact labels through majority voting. This joint approach leverage
The proliferation of academic papers and the development of large language models create an opportunity for more sophisticated impact assessment tools.
Improved methods for evaluating research impact can accelerate scientific progress by better identifying influential work and researchers.
Traditional metrics of academic impact are supplemented by LLM-driven contextual analysis, offering a more nuanced understanding of a publication's contribution.
- · researchers in interdisciplinary fields
- · science funding bodies
- · academic publishers
- · AI/ML researchers
- · citation count-based ranking systems
- · researchers in niche fields with low citation counts
Research evaluation becomes more nuanced and context-aware, moving beyond simple citation counts.
Funding and career progression for academics may increasingly incorporate LLM-driven impact assessments, potentially reshaping research incentives.
The definition of 'impact' within academia could evolve, prioritizing contextual relevance and interdisciplinary influence over raw popularity.
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Read at arXiv cs.CL