Large Language Models (LLMs) are increasingly employed in zero-shot documents
ranking, yielding commendable results. However, several significant challenges
still persist in LLMs for ranking: (1) LLMs are constrained by limited input
length, precluding them from processing a large number of documents
simultaneously; (2) The output document sequence is influenced by the input
order of documents, resulting in inconsistent ranking outcomes; (3) Achieving a
balance between cost and ranking performance is quite challenging. To tackle
these issues, we introduce a novel documents ranking method called TourRank,
which is inspired by the tournament mechanism. This approach alleviates the
impact of LLM's limited input length through intelligent grouping, while the
tournament-like points system ensures robust ranking, mitigating the influence
of the document input sequence. We test TourRank with different LLMs on the
TREC DL datasets and the BEIR benchmark. Experimental results show that
TourRank achieves state-of-the-art performance at a reasonable cost.

通过智能分组减轻 LLM 输入长度的限制，采用类似锦标赛的积分系统确保鲁棒的排序，我们引入了一种名为 TourRank 的新型文件排名方法，实验证明 TourRank 在合理的成本下实现了最先进的性能。