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Recently, substantial progress has been made in text ranking based onpretrained language models such as BERT. However, there are limited studies onhow to leverage more powerful sequence-to-sequence models such as T5. Existingattempts usually formulate text ranking as classification and rely onpostprocessing to obtain a ranked list. In this paper, we propose RankT5 andstudy two T5-based ranking model structures, an encoder-decoder and anencoder-only one, so that they not only can directly output ranking scores foreach query-document pair, but also can be fine-tuned with "pairwise" or"listwise" ranking losses to optimize ranking performances. Our experimentsshow that the proposed models with ranking losses can achieve substantialranking performance gains on different public text ranking data sets. Moreover,when fine-tuned with listwise ranking losses, the ranking model appears to havebetter zero-shot ranking performance on out-of-domain data sets compared to themodel fine-tuned with classification losses.

RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses