Vector-based retrieval systems have become a common staple for academic and
industrial search applications because they provide a simple and scalable way
of extending the search to leverage contextual representations for documents
and queries. As these vector-based systems rely on contextual language models,
their usage commonly requires GPUs, which can be expensive and difficult to
manage. Given recent advances in introducing sparsity into language models for
improved inference efficiency, in this paper, we study how sparse language
models can be used for dense retrieval to improve inference efficiency. Using
the popular retrieval library Tevatron and the MSMARCO, NQ, and TriviaQA
datasets, we find that sparse language models can be used as direct
replacements with little to no drop in accuracy and up to 4.3x improved
inference speeds

研究使用稀疏语言模型替代矢量检索系统来提高推理效率，实验结果表明使用 MSMARCO 数据集，精确度几乎没下降，推理速度最多提高了 4.3 倍。

密集稀疏检索：使用稀疏语言模型进行推理高效密集检索

Dense Sparse Retrieval: Using Sparse Language Models for Inference  Efficient Dense Retrieval

Query reformulations have long been a key mechanism to alleviate the
vocabulary-mismatch problem in information retrieval, for example by expanding
the queries with related query terms or by generating paraphrases of the
queries. In this work, we propose a deep reinforced query reformulation (DRQR)
model to automatically generate new reformulations of the query. To encourage
the model to generate queries which can achieve high performance when
performing the retrieval task, we incorporate query performance prediction into
our reward function. In addition, to evaluate the quality of the reformulated
query in the context of information retrieval, we first train our DRQR model,
then apply the retrieval ranking model on the obtained reformulated query.
Experiments are conducted on the TREC 2020 Deep Learning track MSMARCO document
ranking dataset. Our results show that our proposed model outperforms several
query reformulation model baselines when performing retrieval task. In
addition, improvements are also observed when combining with various retrieval
models, such as query expansion and BERT.

通过使用深度强化学习模型，本研究提出了一个自动生成措辞的查询改写模型。在模型中结合了查询性能预测方法以产生高效的查询。并且针对信息检索的情境下，运用了信息检索排名模型训练，并在 TREC 2020 Deep Learning track MSMARCO 文献排名数据集上进行实验。结果表明，与各种基线模型相比，本研究的模型性能得到了较大提高。