While current NL2SQL tasks constructed using Foundation Models have achieved
commendable results, their direct application to Natural Language to Graph
Query Language (NL2GQL) tasks poses challenges due to the significant
differences between GQL and SQL expressions, as well as the numerous types of
GQL. Our extensive experiments reveal that in NL2GQL tasks, larger Foundation
Models demonstrate superior cross-schema generalization abilities, while
smaller Foundation Models struggle to improve their GQL generation capabilities
through fine-tuning. However, after fine-tuning, smaller models exhibit better
intent comprehension and higher grammatical accuracy. Diverging from rule-based
and slot-filling techniques, we introduce R3-NL2GQL, which employs both smaller
and larger Foundation Models as reranker, rewriter and refiner. The approach
harnesses the comprehension ability of smaller models for information reranker
and rewriter, and the exceptional generalization and generation capabilities of
larger models to transform input natural language queries and code structure
schema into any form of GQLs. Recognizing the lack of established datasets in
this nascent domain, we have created a bilingual dataset derived from graph
database documentation and some open-source Knowledge Graphs (KGs). We tested
our approach on this dataset and the experimental results showed that delivers
promising performance and robustness.Our code and dataset is available at
this https URL

当前的基础模型构建的 NL2SQL 任务在自然语言到图查询语言（NL2GQL）任务中的直接应用存在挑战，我们引入了 R3-NL2GQL 方法，使用较小和较大的基础模型作为重新排序器、重写器和细化器，同时考虑到了 GQL 的差异和较多的类型，我们的实验结果表明该方法在 NL2GQL 任务中具有产生 GQL 的优异能力、更好的意图理解和高精确度的语法。

$R^3$-NL2GQL: 一种综合模型方法，用于提高准确性和减轻幻觉

$R^3$-NL2GQL: A Hybrid Models Approach for for Accuracy Enhancing and  Hallucinations Mitigation

Personalized news recommendation aims to assist users in finding news
articles that align with their interests, which plays a pivotal role in
mitigating users' information overload problem. Although many recent works have
been studied for better user and news representations, the following challenges
have been rarely studied: (C1) How to precisely comprehend a range of intents
coupled within a news article? and (C2) How to differentiate news articles with
varying post-read preferences in users' click history? To tackle both
challenges together, in this paper, we propose a novel personalized news
recommendation framework (CIDER) that employs (1) category-guided intent
disentanglement for (C1) and (2) consistency-based news representation for
(C2). Furthermore, we incorporate a category prediction into the training
process of CIDER as an auxiliary task, which provides supplementary supervisory
signals to enhance intent disentanglement. Extensive experiments on two
real-world datasets reveal that (1) CIDER provides consistent performance
improvements over seven state-of-the-art news recommendation methods and (2)
the proposed strategies significantly improve the model accuracy of CIDER.

我们提出了一个新颖的个性化新闻推荐框架 (CIDER)，通过利用类别导向的意图分离和基于一致性的新闻表示来解决用户的信息过载问题，并且将类别预测作为辅助任务加入到训练过程中，提供了补充监督信号以增强意图分离，实验证明了 CIDER 相对于七种最先进的新闻推荐方法的一致性性能改进和模型准确性的显著提升。