用大模型自动建模工业级约束问题，准确率超基线2倍。
ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming
- 基于思维树框架，融合感知约束的检索模块提升上下文学习能力。
- 在新构建的140个工业级任务集上，准确率领先基线2倍。
- 首个专为约束规划设计的大模型，适合工业优化场景应用。
约束规划（CP）是解决现实世界约束优化问题的关键技术，具备丰富的建模语义和高效求解能力。利用大语言模型（LLM）自动生成形式化建模方案正成为一种有前景的方法，旨在结合符号求解器构建可信的神经符号AI。然而，相较于基于运筹学（OR）模型的研究，CP仍受关注较少。本文提出ConstraintLLM，首个专为约束规划建模设计的LLM，基于开源大模型通过多指令监督微调训练。提出约束感知检索模块（CARM），增强上下文学习能力，并集成至思维树（ToT）框架中，支持引导式自纠正。此外，构建并发布首个工业级基准IndusCP，包含来自多个领域的140个挑战性任务。实验表明，ConstraintLLM在多个基准上达到最优求解准确率，在新发布的IndusCP基准上相较基线提升2倍。代码与数据已公开：https://github.com/william4s/ConstraintLLM。
原文摘要 · Abstract (English)
Constraint programming (CP) is a crucial technology for solving real-world constraint optimization problems (COPs), with the advantages of rich modeling semantics and high solving efficiency. Using large language models (LLMs) to generate formal modeling automatically for COPs is becoming a promising approach, which aims to build trustworthy neuro-symbolic AI with the help of symbolic solvers. However, CP has received less attention compared to works based on operations research (OR) models. We introduce ConstraintLLM, the first LLM specifically designed for CP modeling, which is trained on an open-source LLM with multi-instruction supervised fine-tuning. We propose the Constraint-Aware Retrieval Module (CARM) to increase the in-context learning capabilities, which is integrated in a Tree-of-Thoughts (ToT) framework with guided self-correction mechanism. Moreover, we construct and release IndusCP, the first industrial-level benchmark for CP modeling, which contains 140 challenging tasks from various domains. Our experiments demonstrate that ConstraintLLM achieves state-of-the-art solving accuracy across multiple benchmarks and outperforms the baselines by 2x on the new IndusCP benchmark. Code and data are available at: https://github.com/william4s/ConstraintLLM.
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