用图神经网络和大模型结合，精准识别企业间供应链与竞争关系。
InterCorpRel-LLM: Enhancing Financial Relational Understanding with Graph-Language Models
- 融合图网络与大模型，同时捕捉企业关系的结构与语义。
- 在供应链关系识别任务中F分数达0.8543，远超基线模型。
- 零样本识别竞争对手，适合金融分析与风险管控场景。
识别企业间的供应链与竞争关系对金融分析与公司治理至关重要，但受制于数据规模大、稀疏性强及上下文依赖性高。传统图方法缺乏语义深度，大语言模型难以建模关系依赖。为此，我们提出InterCorpRel-LLM，一个跨模态框架，将图神经网络与大语言模型结合，并基于FactSet供应链数据构建专属数据集，设计公司图匹配、行业分类和供应关系预测三项训练任务。该设计实现结构与语义的联合建模。实验表明，尽管仅使用70亿参数的骨干模型和轻量训练，其在供应链关系识别任务中的F-score达到0.8543，显著优于包括GPT-5在内的强基线（0.2287）。模型还具备零样本竞争对手识别能力，体现对复杂企业动态的捕捉力。本框架为分析师和战略决策者提供可靠工具，助力动态市场中的决策与风险管理。
原文摘要 · Abstract (English)
Identifying inter-firm relationships such as supply and competitive ties is critical for financial analysis and corporate governance, yet remains challenging due to the scale, sparsity, and contextual dependence of corporate data. Graph-based methods capture structure but miss semantic depth, while large language models (LLMs) excel at text but remain limited in their ability to represent relational dependencies. To address this, we propose InterCorpRel-LLM, a cross-modal framework that integrates GNNs with LLMs, supported by a proprietary dataset derived from FactSet supply chain records and three tailored training tasks: company graph matching, industry classification, and supply relation prediction. This design enables effective joint modeling of structure and semantics. Experiments show that InterCorpRel-LLM substantially outperforms strong baselines, including GPT-5, on a supply relation identification task, achieving an F-score of 0.8543 vs. 0.2287 with only a 7B-parameter backbone and lightweight training. The model also generalizes to zero-shot competitor identification, underscoring its ability to capture nuanced inter-firm dynamics. Our framework thus provides analysts and strategists with a robust tool for mapping and reasoning about complex corporate networks, enhancing decision-making and risk management in dynamic markets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。