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20212025
most citedSolving General Natural-Language-Description Optimization Problems with Large Language Models

2 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2025

One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction

Ben Liu, Jihan Zhang, Fangquan Lin +2

Large language models (LLMs) have been increasingly employed in various intelligent educational systems, simulating human tutors to facilitate effective human-machine interaction.…

cs.AI2025

SymAgent: A Neural-Symbolic Self-Learning Agent Framework for Complex Reasoning over Knowledge Graphs

Ben Liu, Jihai Zhang, Fangquan Lin +3

Recent advancements have highlighted that Large Language Models (LLMs) are prone to hallucinations when solving complex reasoning problems, leading to erroneous results. To tackle…

cs.CL2024★ 2 cited

Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion

Ben Liu, Jihai Zhang, Fangquan Lin +2

Large Language Models (LLMs) present massive inherent knowledge and superior semantic comprehension capability, which have revolutionized various tasks in natural language processi…

math.OC2024★ 2 cited

Solving General Natural-Language-Description Optimization Problems with Large Language Models

Jihai Zhang, Wei Wang, Siyan Guo +4

Optimization problems seek to find the best solution to an objective under a set of constraints, and have been widely investigated in real-world applications. Modeling and solving…

eess.SY2023

A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty

Wei Jiang, Zhongkai Yi, Li Wang +4

Aggregating distributed energy resources in power systems significantly increases uncertainties, in particular caused by the fluctuation of renewable energy generation. This issue…

cs.LG2022

Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation

Miao Xiong, Shen Li, Wenjie Feng +3

How do we know when the predictions made by a classifier can be trusted? This is a fundamental problem that also has immense practical applicability, especially in safety-critical…

cs.LG2021

Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting

Longyuan Li, Jihai Zhang, Junchi Yan +4

Time-series is ubiquitous across applications, such as transportation, finance and healthcare. Time-series is often influenced by external factors, especially in the form of asynch…