Plain-language summary
Large collections of petitions can be difficult to review and prioritize manually. LLMPR represents each petition using several language-model embeddings and uses transfer-learning and ranking models to identify semantically important cases.
Research contribution
- Comparison and combination of multiple transformer embedding families.
- Transfer-learning features with classical and ensemble prediction models.
- Evaluation using representation-quality and ranking metrics.
Publication status
Public preprint available as arXiv:2505.21689.
Code and dataset
Code and dataset links will be added when a public release is available.
Recommended citation
Gayen, A., Chakraborty, S., Sen, M., Paul, S., and Jana, A. (2025). LLMPR: A Novel LLM-Driven Transfer Learning based Petition Ranking Model. arXiv:2505.21689.
BibTeX
@article{gayen2025llmpr,
title={LLMPR: A Novel LLM-Driven Transfer Learning based Petition Ranking Model},
author={Gayen, Avijit and Chakraborty, Somyajit and Sen, M. and Paul, S. and Jana, Angshuman},
journal={arXiv preprint arXiv:2505.21689},
year={2025}
}