Query expansion has been proved to be effective in improving recall and
precision of first-stage retrievers, and yet its influence on a complicated,
state-of-the-art cross-encoder ranker remains under-explored. We first show
that directly applying the expansion techniques in the current literature to
state-of-the-art neural rankers can result in deteriorated zero-shot
performance. To this end, we propose GFF, a pipeline that includes a large
language model and a neural ranker, to Generate, Filter, and Fuse query
expansions more effectively in order to improve the zero-shot ranking metrics
such as nDCG@10. Specifically, GFF first calls an instruction-following
language model to generate query-related keywords through a reasoning chain.
Leveraging self-consistency and reciprocal rank weighting, GFF further filters
and combines the ranking results of each expanded query dynamically. By
utilizing this pipeline, we show that GFF can improve the zero-shot nDCG@10 on
BEIR and TREC DL 2019/2020. We also analyze different modelling choices in the
GFF pipeline and shed light on the future directions in query expansion for
zero-shot neural rankers.

查询扩展对于复杂的现代交叉编码器排名器的影响尚未得到充分探索，本研究提出了 GFF 管道，通过生成、过滤和融合查询扩展更有效地改善零样本排名指标，进而改进了 BEIR 和 TREC DL 2019/2020 上的零样本 nDCG@10。

生成、过滤和融合：零样本神经排序器的多步关键词扩展

Generate, Filter, and Fuse: Query Expansion via Multi-Step Keyword  Generation for Zero-Shot Neural Rankers

Sequential Recommender Systems (SRSs) are a popular type of recommender
system that learns from a user's history to predict the next item they are
likely to interact with. However, user interactions can be affected by noise
stemming from account sharing, inconsistent preferences, or accidental clicks.
To address this issue, we (i) propose a new evaluation protocol that takes
multiple future items into account and (ii) introduce a novel relevance-aware
loss function to train a SRS with multiple future items to make it more robust
to noise. Our relevance-aware models obtain an improvement of ~1.2% of NDCG@10
and 0.88% in the traditional evaluation protocol, while in the new evaluation
protocol, the improvement is ~1.63% of NDCG@10 and ~1.5% of HR w.r.t the best
performing models.

提出了一个新的评估协议来解决 “多余噪声” 对顺序推荐系统的影响，并引入了一种新型的关联感知损失函数来训练该系统，使其更具有鲁棒性，该模型在传统评估协议中 NDCG@10 有 0.88% 的提高，在新评估协议中有 1.63% 的 NDCG@10 和 1.5％的 HR 提高。