CRISP: Characterizing Relative Impact of Scholarly Publications
Summary: arXiv:2603.26791v1 Announce Type: cross
Assessing the impact of scholarly publications has long posed challenges for researchers, particularly when attempting to understand the relative significance of cited works within a single citing paper. Traditional approaches often evaluate a cited paper’s impact based solely on its citation context in isolation, which limits the ability to make comparisons across multiple works. In response to this challenge, a new method known as CRISP has been proposed to enhance the evaluation of citation impact.
Introduction to CRISP
CRISP, an acronym for Characterizing Relative Impact of Scholarly Publications, employs advanced techniques utilizing large language models (LLMs) to jointly rank all cited papers within a citing document. This innovative approach is designed to overcome the limitations of traditional citation impact assessments by providing a more holistic view of the citation landscape.
Methodology
- Joint Ranking: Unlike previous methods that focused on individual citations, CRISP evaluates all citations together, allowing for relative comparisons across the entire set of references.
- Randomized Ordering: To address the positional bias inherent in LLMs, CRISP ranks citation lists in a randomized order multiple times. This ensures that the rankings are not skewed by the position of citations within the text.
- Majority Voting: The impact labels derived from the multiple rankings are aggregated through a majority voting mechanism, which enhances the reliability of the final impact assessments.
Results and Performance
The performance of CRISP has been rigorously evaluated against a dataset of human-annotated citations. The results indicate that CRISP significantly outperforms previous state-of-the-art impact classifiers. Notably, CRISP achieved an impressive:
- +9.5% increase in accuracy.
- +8.3% improvement in F1 score.
These enhancements illustrate CRISP’s capability to more reliably distinguish impactful references, thereby providing researchers with a more effective tool for citation impact analysis.
Efficiency and Scalability
In addition to its improved accuracy, CRISP also offers enhanced efficiency. By reducing the number of LLM calls required for analysis, CRISP not only streamlines the process but also makes it more cost-effective. This efficiency gain is particularly valuable for large-scale citation impact assessments, making CRISP an appealing option for researchers and institutions alike.
Future Directions
The developers of CRISP have committed to supporting ongoing research in this area by releasing their rankings, impact labels, and codebase. This open approach will foster collaboration and innovation in the field of citation analysis, enabling other researchers to build upon the foundation laid by CRISP.
Conclusion
CRISP represents a significant advancement in the field of scholarly publication impact assessment. By leveraging the capabilities of large language models and adopting a joint ranking methodology, CRISP provides a more comprehensive and reliable analysis of citation impact. As more researchers adopt this innovative approach, the potential for improved understanding of scholarly influence will continue to grow.