Supervised ranking methods based on bi-encoder or cross-encoder architectures
have shown success in multi-stage text ranking tasks, but they require large
amounts of relevance judgments as training data. In this work, we propose
Listwise Reranker with a Large Language Model (LRL), which achieves strong
reranking effectiveness without using any task-specific training data.
Different from the existing pointwise ranking methods, where documents are
scored independently and ranked according to the scores, LRL directly generates
a reordered list of document identifiers given the candidate documents.
Experiments on three TREC web search datasets demonstrate that LRL not only
outperforms zero-shot pointwise methods when reranking first-stage retrieval
results, but can also act as a final-stage reranker to improve the top-ranked
results of a pointwise method for improved efficiency. Additionally, we apply
our approach to subsets of MIRACL, a recent multilingual retrieval dataset,
with results showing its potential to generalize across different languages.

在 TREC 网络搜索数据集和 MIRACL 跨语言数据集上，使用无需特定任务训练数据的 Listwise Reranker with a Large Language Model (LRL) 可以作为第一阶段和最终阶段的重新排序方法，在多层文本排序任务中取得了强大的效果。