UNCO: Towards Unifying Neural Combinatorial Optimization through Large Language Model
<|reference_start|>UNCO: Towards Unifying Neural Combinatorial Optimization through Large Language Model: Recently, applying neural networks to address combinatorial optimization problems (COPs) has attracted considerable research attention. The prevailing methods always train deep models independently on specific problems, lacking a unified framework for concurrently tackling various COPs. To this end, we propose a unified neural combinatorial optimization (UNCO) framework to solve different types of COPs by a single model. Specifically, we use natural language to formulate text-attributed instances for different COPs and encode them in the same embedding space by the large language model (LLM). The obtained embeddings are further advanced by an encoder-decoder model without any problem-specific modules, thereby facilitating a unified process of solution construction. We further adopt the conflict gradients erasing reinforcement learning (CGERL) algorithm to train the UNCO model, delivering better performance across different COPs than vanilla multi-objective learning. Experiments show that the UNCO model can solve multiple COPs after a single-session training, and achieves satisfactory performance that is comparable to several traditional or learning-based baselines. Instead of pursuing the best performance for each COP, we explore the synergy between tasks and few-shot generalization based on LLM to inspire future work.<|reference_end|>
@article{jiang2024unco:,
    title={UNCO: Towards Unifying Neural Combinatorial Optimization through Large
  Language Model},
    author={Xia Jiang, Yaoxin Wu, Yuan Wang, Yingqian Zhang},
    journal={arXiv preprint arXiv:2408.12214},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12214},
    primaryClass={cs.AI}
}