Large Language Models (LLMs) have demonstrated superior listwise ranking performance. However, their superior performance often relies on large-scale parameters (\eg, GPT-4) and a repetitive sliding window process, which introduces significant efficiency challenges. In this paper, we propose \textbf{CoRanking}, a novel collaborative ranking framework that combines small and large ranking models for efficient and effective ranking. CoRanking first employs a small-size reranker to pre-rank all the candidate passages, bringing relevant ones to the top part of the list (\eg, top-20). Then, the LLM listwise reranker is applied to only rerank these top-ranked passages instead of the whole list, substantially enhancing overall ranking efficiency. Although more efficient, previous studies have revealed that the LLM listwise reranker have significant positional biases on the order of input passages. Directly feed the top-ranked passages from small reranker may result in the sub-optimal performance of LLM listwise reranker. To alleviate this problem, we introduce a passage order adjuster trained via reinforcement learning, which reorders the top passages from the small reranker to align with the LLM's preferences of passage order. Extensive experiments on three IR benchmarks demonstrate that CoRanking significantly improves efficiency (reducing ranking latency by about 70\%) while achieving even better effectiveness compared to using only the LLM listwise reranker.

翻译：大型语言模型（LLM）已展现出卓越的列表式排序性能。然而，其优越性能通常依赖于大规模参数（例如GPT-4）和重复的滑动窗口处理过程，这带来了显著的效率挑战。本文提出\textbf{协同排序}，一种新颖的协作排序框架，通过结合小型与大型排序模型实现高效且有效的排序。协同排序首先采用小规模重排序器对所有候选段落进行预排序，将相关段落提升至列表顶部区域（如前20位）。随后，LLM列表式重排序器仅对这些顶部段落而非整个列表进行重排序，从而显著提升整体排序效率。尽管效率更高，先前研究表明LLM列表式重排序器对输入段落的顺序存在显著位置偏差。若直接将小型重排序器输出的顶部段落输入LLM，可能导致其列表式重排序器性能次优。为缓解此问题，我们引入一种通过强化学习训练的段落顺序调整器，该调整器对小型重排序器输出的顶部段落进行重新排序，以契合LLM对段落顺序的偏好。在三个信息检索基准上的大量实验表明，协同排序在显著提升效率（降低约70\%排序延迟）的同时，相比仅使用LLM列表式重排序器取得了更优的排序效果。