Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization
This addresses the challenge of applying LLMs to real-world optimization tasks, though it appears incremental as it builds on existing methods for integrating LLMs with optimization.
The paper tackled the problem of solving combinatorial optimization problems (COPs) with text attributes using large language models (LLMs), resulting in a framework that achieves state-of-the-art performance across diverse problems.
To advance capabilities of large language models (LLMs) in solving combinatorial optimization problems (COPs), this paper presents the Language-based Neural COP Solver (LNCS), a novel framework that is unified for the end-to-end resolution of diverse text-attributed COPs. LNCS leverages LLMs to encode problem instances into a unified semantic space, and integrates their embeddings with a Transformer-based solution generator to produce high-quality solutions. By training the solution generator with conflict-free multi-task reinforcement learning, LNCS effectively enhances LLM performance in tackling COPs of varying types and sizes, achieving state-of-the-art results across diverse problems. Extensive experiments validate the effectiveness and generalizability of the LNCS, highlighting its potential as a unified and practical framework for real-world COP applications.