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RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses
Research article (Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023) · cited 65× · AI/ML
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses. Retrieved May 24, 2026, from https://4ort.xyz/entity/rankt5-fine-tuning-t5-for-text-ranking-with-ranking-losses
MLA“RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/rankt5-fine-tuning-t5-for-text-ranking-with-ranking-losses.
BibTeX@misc{4ortxyz_rankt5-fine-tuning-t5-for-text-ranking-with-ranking-losses_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses}}, year = {2026}, url = {https://4ort.xyz/entity/rankt5-fine-tuning-t5-for-text-ranking-with-ranking-losses}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses — https://4ort.xyz/entity/rankt5-fine-tuning-t5-for-text-ranking-with-ranking-losses (retrieved 2026-05-24)