Yuanyuan Liang

Yuanyuan Liang, Tingyu Xie, Gan Peng et al.

The emergence of Large Language Models (LLMs) has revolutionized many fields, not only traditional natural language processing (NLP) tasks. Recently, research on applying LLMs to the database field has been booming, and as a typical non-relational database, the use of LLMs in graph database research has naturally gained significant attention. Recent efforts have increasingly focused on leveraging LLMs to translate natural language into graph query language (NL2GQL). Although some progress has been made, these methods have clear limitations, such as their reliance on streamlined processes that often overlook the potential of LLMs to autonomously plan and collaborate with other LLMs in tackling complex NL2GQL challenges. To address this gap, we propose NAT-NL2GQL, a novel multi-agent framework for translating natural language to graph query language. Specifically, our framework consists of three synergistic agents: the Preprocessor agent, the Generator agent, and the Refiner agent. The Preprocessor agent manages data processing as context, including tasks such as name entity recognition, query rewriting, path linking, and the extraction of query-related schemas. The Generator agent is a fine-tuned LLM trained on NL-GQL data, responsible for generating corresponding GQL statements based on queries and their related schemas. The Refiner agent is tasked with refining the GQL or context using error information obtained from the GQL execution results. Given the scarcity of high-quality open-source NL2GQL datasets based on nGQL syntax, we developed StockGQL, a dataset constructed from a financial market graph database. It is available at: https://github.com/leonyuancode/StockGQL. Experimental results on the StockGQL and SpCQL datasets reveal that our method significantly outperforms baseline approaches, highlighting its potential for advancing NL2GQL research.

Yurui Ming, Yuanyuan Liang

We report the possibility of using a simple neural network for effortless restoration of low-light images inspired by the retina model, which mimics the neurophysiological principles and dynamics of various types of optical neurons. The proposed neural network model saves the cost of computational overhead in contrast with traditional signal-processing models, and generates results comparable with complicated deep learning models from the subjective perceptual perspective. This work shows that to directly simulate the functionalities of retinal neurons using neural networks not only avoids the manually seeking for the optimal parameters, but also paves the way to build corresponding artificial versions for certain neurobiological organizations.

Yuanyuan Liang, Lei Pan, Tingyu Xie et al.

In recent years, research on transforming natural language into graph query language (NL2GQL) has been increasing. Most existing methods focus on single-turn transformation from NL to GQL. In practical applications, user interactions with graph databases are typically multi-turn, dynamic, and context-dependent. While single-turn methods can handle straightforward queries, more complex scenarios often require users to iteratively adjust their queries, investigate the connections between entities, or request additional details across multiple dialogue turns. Research focused on single-turn conversion fails to effectively address multi-turn dialogues and complex context dependencies. Additionally, the scarcity of high-quality multi-turn NL2GQL datasets further hinders the progress of this field. To address this challenge, we propose an automated method for constructing multi-turn NL2GQL datasets based on Large Language Models (LLMs) , and apply this method to develop the MTGQL dataset, which is constructed from a financial market graph database and will be publicly released for future research. Moreover, we propose three types of baseline methods to assess the effectiveness of multi-turn NL2GQL translation, thereby laying a solid foundation for future research.

Yuanyuan Liang, Tianhao Zhang, Tingyu Xie

Handling anomalies is a critical preprocessing step in multivariate time series prediction. However, existing approaches that separate anomaly preprocessing from model training for multivariate time series prediction encounter significant limitations. Specifically, these methods fail to utilize auxiliary information crucial for identifying latent anomalies associated with spatiotemporal factors during the preprocessing stage. Instead, they rely solely on data distribution for anomaly detection, which can result in the incorrect processing of numerous samples that could otherwise contribute positively to model training. To address this, we propose STTS-EAD, an end-to-end method that seamlessly integrates anomaly detection into the training process of multivariate time series forecasting and aims to improve Spatio-Temporal learning based Time Series prediction via Embedded Anomaly Detection. Our proposed STTS-EAD leverages spatio-temporal information for forecasting and anomaly detection, with the two parts alternately executed and optimized for each other. To the best of our knowledge, STTS-EAD is the first to integrate anomaly detection and forecasting tasks in the training phase for improving the accuracy of multivariate time series forecasting. Extensive experiments on a public stock dataset and two real-world sales datasets from a renowned coffee chain enterprise show that our proposed method can effectively process detected anomalies in the training stage to improve forecasting performance in the inference stage and significantly outperform baselines.