75 citations · 87 across the 15 of their papers we have counts for
9 papers · 1 filter
BIRD-History: A Benchmark for History-Driven Text-to-SQL with Fine-Grained Knowledge Annotations
Yunfan Zhou, Qiming Shi, Yizhou Yang +2
While recent Large Language Model (LLM)-based text-to-SQL systems achieve impressive performance on standard benchmarks, they struggle when user queries implicitly rely on domain-s…
AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agentic Data Analysis
Yangtian Liu, Yan Miao, Shuhan Liu +4
Large language models are pushing data science toward increasingly autonomous and agentic workflows, with recent systems already supporting multi-step and long-running analyses. As…
POEM: Phase-Aware Feature Rotation for Time Series Forecasting Under Periodicity Drift
Jiawen Zhu, Shuhan Liu, Shengxuan Li +2
Deep learning has advanced time series forecasting, but periodicity drift, in which cycle timing and phase vary over time, remains a challenging problem. Existing methods predomina…
MerchantBench: Benchmarking LLM Agents for Long-Term Coherence in E-Commerce Operations
Qiming Shi, Yulong Tao, Linbo Jin +10
Large language model agents are increasingly evaluated as autonomous tool users, yet most benchmarks focus on bounded tasks with immediate success criteria. Real-world deployments…
SKILL-KD: Contrastive Skill Distillation for LLM Agents
Qiming Shi, Yibo Dou, Jiawen Zhu +5
Skill-based prompting has become a practical mechanism for improving LLM agents, yet existing methods often treat skills as summaries of the agent's own experience or of successful…
SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering
Qiming Shi, Zhaolu Kang, Yunfan Zhou +2
Large language models are increasingly deployed as tool-augmented agents to acquire information beyond parametric knowledge. While recent work has improved long-horizon tool-use re…
Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting
Jiawen Zhu, Shuhan Liu, Di Weng +1
Non-stationary time series forecasting is challenged by evolving distribution shifts that static models struggle to capture. While Mixture-of-Experts (MoE) architectures offer a pr…
KEditVis: A Visual Analytics System for Knowledge Editing of Large Language Models
Zhenning Chen, Hanbei Zhan, Yanwei Huang +4
Large Language Models (LLMs) demonstrate exceptional capabilities in factual question answering, yet they sometimes provide incorrect responses. To address this issue, knowledge ed…
Cerebra: Aligning Implicit Knowledge in Interactive SQL Authoring
Yunfan Zhou, Qiming Shi, Zhongsu Luo +5
LLM-driven tools have significantly lowered barriers to writing SQL queries. However, user instructions are often underspecified, assuming the model understands implicit knowledge,…