  While large-scale pre-trained language models like BERT have advanced the
state-of-the-art in IR, its application in query performance prediction (QPP)
is so far based on pointwise modeling of individual queries. Meanwhile, recent
studies suggest that the cross-attention modeling of a group of documents can
effectively boost performances for both learning-to-rank algorithms and
BERT-based re-ranking. To this end, a BERT-based groupwise QPP model is
proposed, in which the ranking contexts of a list of queries are jointly
modeled to predict the relative performance of individual queries. Extensive
experiments on three standard TREC collections showcase effectiveness of our
approach. Our code is available at https://github.com/VerdureChen/Group-QPP.
