3 citations · 3 across the 16 of their papers we have counts for
12 papers · 1 filter
OptiTree: Hierarchical Thoughts Generation with Tree Search for LLM Optimization Modeling
Haoyang Liu, Jie Wang, Yuyang Cai +3
Optimization modeling is one of the most crucial but technical parts of operations research (OR). To automate the modeling process, existing works have leveraged large language mod…
REG: A Regularization Optimizer for Robust Training Dynamics
Zehua Liu, Han Wu, Xiaojin Fu +4
Optimizers are crucial for the efficient training of Large Language Models (LLMs). While AdamW is the de facto standard, recent structure-aware optimizers like Muon have emerged, w…
SAC-Opt: Semantic Anchors for Iterative Correction in Optimization Modeling
Yansen Zhang, Qingcan Kang, Yujie Chen +5
Large language models (LLMs) have opened new paradigms in optimization modeling by enabling the generation of executable solver code from natural language descriptions. Despite thi…
A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions
Ziyang Xiao, Jingrong Xie, Lilin Xu +15
By virtue of its great utility in solving real-world problems, optimization modeling has been widely employed for optimal decision-making across various sectors, but it requires su…
Activation-Guided Consensus Merging for Large Language Models
Yuxuan Yao, Shuqi Liu, Zehua Liu +6
Recent research has increasingly focused on reconciling the reasoning capabilities of System 2 with the efficiency of System 1. While existing training-based and prompt-based appro…
MoLAE: Mixture of Latent Experts for Parameter-Efficient Language Models
Zehua Liu, Han Wu, Ruifeng She +4
Mixture of Experts (MoE) has become a key architectural paradigm for efficiently scaling Large Language Models (LLMs) by selectively activating a subset of parameters for each inpu…
Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging
Han Wu, Yuxuan Yao, Shuqi Liu +7
The transition from System 1 to System 2 reasoning in large language models (LLMs) has marked significant advancements in handling complex tasks through deliberate, iterative think…
Sens-Merging: Sensitivity-Guided Parameter Balancing for Merging Large Language Models
Shuqi Liu, Han Wu, Bowei He +3
Recent advances in large language models have led to numerous task-specialized fine-tuned variants, creating a need for efficient model merging techniques that preserve specialized…
Decision Information Meets Large Language Models: The Future of Explainable Operations Research
Yansen Zhang, Qingcan Kang, Wing Yin Yu +5
Operations Research (OR) is vital for decision-making in many industries. While recent OR methods have seen significant improvements in automation and efficiency through integratin…
ARS: Automatic Routing Solver with Large Language Models
Kai Li, Fei Liu, Zhenkun Wang +4
Real-world Vehicle Routing Problems (VRPs) are characterized by a variety of practical constraints, making manual solver design both knowledge-intensive and time-consuming. Althoug…
LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging
Zehua Liu, Han Wu, Yuxuan Yao +4
While most current approaches rely on further training techniques, such as fine-tuning or reinforcement learning, to enhance model capacities, model merging stands out for its abil…
1bit-Merging: Dynamic Quantized Merging for Large Language Models
Shuqi Liu, Yuxuan Yao, Bowei He +5
Recent advances in large language models have led to specialized models excelling in specific domains, creating a need for efficient model merging techniques. While traditional mer…