48 citations · 166 across the 31 of their papers we have counts for
33 papers
ZooWork-ShopRanker: An Open, Preference-Aligned E-Commerce Reranker
Siqiao Xue, Shuxuan Liu, Ning Hu
Open rerankers trained for general web retrieval transfer imperfectly to e-commerce, where ranking decisions depend not only on topical relevance but also on user preferences, prod…
Beyond Retrieval: A Multitask Benchmark and Model for Code Search
Siqiao Xue, Zihan Liao, Jin Qin +4
Code search has usually been evaluated as first-stage retrieval, even though production systems rely on broader pipelines with reranking and developer-style queries. Existing bench…
QuitoBench: A High-Quality Open Time Series Forecasting Benchmark
Siqiao Xue, Zhaoyang Zhu, Wei Zhang +7
Time series forecasting is critical across finance, healthcare, and cloud computing, yet progress is constrained by a fundamental bottleneck: the scarcity of large-scale, high-qual…
LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval
Gensmo. ai, Chao Gao, Siqiao Xue +4
In this paper, we present LookBench (We use the term "look" to reflect retrieval that mirrors how people shop -- finding the exact item, a close substitute, or a visually consisten…
DORAEMON: A Unified Library for Visual Object Modeling and Representation Learning at Scale
Ke Du, Yimin Peng, Chao Gao +2
DORAEMON is an open-source PyTorch library that unifies visual object modeling and representation learning across diverse scales. A single YAML-driven workflow covers classificatio…
Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning
Gangwei Jiang, Caigao Jiang, Zhaoyi Li +5
Catastrophic forgetting (CF) poses a significant challenge in machine learning, where a model forgets previously learned information upon learning new tasks. Despite the advanced c…
ROMAS: A Role-Based Multi-Agent System for Database monitoring and Planning
Yi Huang, Fangyin Cheng, Fan Zhou +7
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in data analytics when integrated with Multi-Agent Systems (MAS). However, these systems oft…
FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering
Siqiao Xue, Xiaojing Li, Fan Zhou +3
In this paper, we introduce FAMMA, an open-source benchmark for \underline{f}in\underline{a}ncial \underline{m}ultilingual \underline{m}ultimodal question \underline{a}nswering (QA…
Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction
Xiaoyu Tan, Yongxin Deng, Chao Qu +4
Recently, models for user representation learning have been widely applied in click-through-rate (CTR) and conversion-rate (CVR) prediction. Usually, the model learns a universal u…
GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting
Fan Zhou, Chen Pan, Lintao Ma +9
Time series forecasts of different temporal granularity are widely used in real-world applications, e.g., sales prediction in days and weeks for making different inventory plans. H…
DB-GPT-Hub: Towards Open Benchmarking Text-to-SQL Empowered by Large Language Models
Fan Zhou, Siqiao Xue, Danrui Qi +8
Large language models (LLMs) becomes the dominant paradigm for the challenging task of text-to-SQL. LLM-empowered text-to-SQL methods are typically categorized into prompting-based…
Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Models
Zhixuan Chu, Lei Zhang, Yichen Sun +4
The rapid advancement in text-to-video (T2V) generative models has enabled the synthesis of high-fidelity video content guided by textual descriptions. Despite this significant pro…
Demonstration of DB-GPT: Next Generation Data Interaction System Empowered by Large Language Models
Siqiao Xue, Danrui Qi, Caigao Jiang +13
The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software. The technologies of interacting with data particularly have an importa…
DB-GPT: Empowering Database Interactions with Private Large Language Models
Siqiao Xue, Caigao Jiang, Wenhui Shi +13
The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software. Database technologies particularly have an important entanglement with…
Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompt
Gangwei Jiang, Caigao Jiang, Siqiao Xue +4
Continual pre-training has been urgent for adapting a pre-trained model to a multitude of domains and tasks in the fast-evolving world. In practice, a continually pre-trained model…
Prompt-augmented Temporal Point Process for Streaming Event Sequence
Siqiao Xue, Yan Wang, Zhixuan Chu +7
Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling continuous-time event sequences, such as user activities on the web and financial transactions. In re…
Deep Optimal Timing Strategies for Time Series
Chen Pan, Fan Zhou, Xuanwei Hu +6
Deciding the best future execution time is a critical task in many business activities while evolving time series forecasting, and optimal timing strategy provides such a solution,…
Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference
Yan Wang, Zhixuan Chu, Tao Zhou +9
Asynchronous time series, also known as temporal event sequences, are the basis of many applications throughout different industries. Temporal point processes(TPPs) are the standar…
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
Ming Jin, Yaxuan Kong, Yuxuan Liang +13
Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications. Generated in massive volumes by physical and virtual sensors, they record d…
CoCA: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending
Shiyi Zhu, Jing Ye, Wei Jiang +4
Self-attention and position embedding are two key modules in transformer-based Large Language Models (LLMs). However, the potential relationship between them is far from well studi…
Leveraging Large Language Models for Pre-trained Recommender Systems
Zhixuan Chu, Hongyan Hao, Xin Ouyang +9
Recent advancements in recommendation systems have shifted towards more comprehensive and personalized recommendations by utilizing large language models (LLM). However, effectivel…
Continual Learning in Predictive Autoscaling
Hongyan Hao, Zhixuan Chu, Shiyi Zhu +7
Predictive Autoscaling is used to forecast the workloads of servers and prepare the resources in advance to ensure service level objectives (SLOs) in dynamic cloud environments. Ho…
Enhancing Recommender Systems with Large Language Model Reasoning Graphs
Yan Wang, Zhixuan Chu, Xin Ouyang +10
Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behavior…
WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine
Siqiao Xue, Fan Zhou, Yi Xu +10
We present WeaverBird, an intelligent dialogue system designed specifically for the finance domain. Our system harnesses a large language model of GPT architecture that has been tu…