Batched Self-Consistency Improves LLM Relevance Assessment and Ranking. Korikov, A., Du, P., Sanner, S., & Rekabsaz, N. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP-25), pages 32675–32691, Suzhou, China, November, 2025. Association for Computational Linguistics.
Paper
Url bibtex 7 downloads @inproceedings{sanner:emnlp25b,
title = "Batched Self-Consistency Improves {LLM} Relevance Assessment and Ranking",
author = "Anton Korikov and Pan Du and Scott Sanner and Navid Rekabsaz",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing ({EMNLP}-25)",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url_paper = {https://ssanner.github.io/papers/emnlp25_batchllm.pdf},
url_url = "https://aclanthology.org/2025.emnlp-main.1661/",
pages = "32675--32691"
}
Downloads: 7
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