Query rewrite, which aims to generate more efficient queries by altering a
SQL query's structure without changing the query result, has been an important
research problem. In order to maintain equivalence between the rewritten query
and the original one during rewriting, traditional query rewrite methods always
rewrite the queries following certain rewrite rules. However, some problems
still remain. Firstly, existing methods of finding the optimal choice or
sequence of rewrite rules are still limited and the process always costs a lot
of resources. Methods involving discovering new rewrite rules typically require
complicated proofs of structural logic or extensive user interactions.
Secondly, current query rewrite methods usually rely highly on DBMS cost
estimators which are often not accurate. In this paper, we address these
problems by proposing a novel method of query rewrite named LLM-R2, adopting a
large language model (LLM) to propose possible rewrite rules for a database
rewrite system. To further improve the inference ability of LLM in recommending
rewrite rules, we train a contrastive model by curriculum to learn query
representations and select effective query demonstrations for the LLM.
Experimental results have shown that our method can significantly improve the
query execution efficiency and outperform the baseline methods. In addition,
our method enjoys high robustness across different datasets.

通过采用大语言模型（LLM）为数据库重写系统提出了一种名为 LLM-R2 的新型查询重写方法，该方法能够极大地提高查询执行效率，并且优于基线方法。

LLM-R2: 改进的基于规则重写系统的大型语言模型用于提升查询效率

LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for  Boosting Query Efficiency

The task of query rewrite aims to convert an in-context query to its
fully-specified version where ellipsis and coreference are completed and
referred-back according to the history context. Although much progress has been
made, less efforts have been paid to real scenario conversations that involve
drawing information from more than one modalities. In this paper, we propose
the task of multimodal conversational query rewrite (McQR), which performs
query rewrite under the multimodal visual conversation setting. We collect a
large-scale dataset named McQueen based on manual annotation, which contains
15k visual conversations and over 80k queries where each one is associated with
a fully-specified rewrite version. In addition, for entities appearing in the
rewrite, we provide the corresponding image box annotation. We then use the
McQueen dataset to benchmark a state-of-the-art method for effectively tackling
the McQR task, which is based on a multimodal pre-trained model with pointer
generator. Extensive experiments are performed to demonstrate the effectiveness
of our model on this task\footnote{The dataset and code of this paper are both
available in https://github.com/yfyuan01/MQR

本文提出了多模态对话查询重写（McQR）任务，并收集了名为 McQueen 的大规模数据集，用于基于多模态预训练模型的指针生成器，实现 McQR 任务的最新方法的基准测试。在对实验数据的实验中，证明了该模型在 McQR 任务中的有效性。

McQueen：一个多模态会话查询改写基准

McQueen: a Benchmark for Multimodal Conversational Query Rewrite

Anaphora and ellipses are two common phenomena in dialogues. Without
resolving referring expressions and information omission, dialogue systems may
fail to generate consistent and coherent responses. Traditionally, anaphora is
resolved by coreference resolution and ellipses by query rewrite. In this work,
we propose a novel joint learning framework of modeling coreference resolution
and query rewriting for complex, multi-turn dialogue understanding. Given an
ongoing dialogue between a user and a dialogue assistant, for the user query,
our joint learning model first predicts coreference links between the query and
the dialogue context, and then generates a self-contained rewritten user query.
To evaluate our model, we annotate a dialogue based coreference resolution
dataset, MuDoCo, with rewritten queries. Results show that the performance of
query rewrite can be substantially boosted (+2.3% F1) with the aid of
coreference modeling. Furthermore, our joint model outperforms the
state-of-the-art coreference resolution model (+2% F1) on this dataset.

本研究提出一种新颖的联合学习框架，用于解决复杂的多轮对话理解中的指代消解和查询重写，通过在进行中的对话中预测查询和对话上下文之间的指代链，并生成自包含的重写查询，可显著提高查询重写的性能，并在数据集 MuDoCo 上优于最先进的指代解析模型。