用大模型指导张量网络秩选择，让非专家也能理解并优化高阶数据分析。
Towards LLM-guided Efficient and Interpretable Multi-linear Tensor Network Rank Selection
- 利用大模型的推理能力辅助选择张量网络的秩
- 在金融高阶数据上验证了可解释性和泛化能力
- 适合无领域背景的研究者快速应用张量分解
我们提出一种新框架，借助大语言模型（LLMs）引导张量网络模型在高阶数据分析中的秩选择。通过利用LLMs的内在推理能力和领域知识，该方法提升了秩选择的可解释性，并能有效优化目标函数。该框架使无专门领域知识的用户也能使用张量网络分解，并理解秩选择背后的逻辑。实验结果在金融高阶数据集上验证了该方法的可解释性推理、对未见测试数据的强大泛化能力，以及在多轮迭代中自我增强的潜力。本工作位于大语言模型与高阶数据分析的交叉领域。
原文摘要 · Abstract (English)
We propose a novel framework that leverages large language models (LLMs) to guide the rank selection in tensor network models for higher-order data analysis. By utilising the intrinsic reasoning capabilities and domain knowledge of LLMs, our approach offers enhanced interpretability of the rank choices and can effectively optimise the objective function. This framework enables users without specialised domain expertise to utilise tensor network decompositions and understand the underlying rationale within the rank selection process. Experimental results validate our method on financial higher-order datasets, demonstrating interpretable reasoning, strong generalisation to unseen test data, and its potential for self-enhancement over successive iterations. This work is placed at the intersection of large language models and higher-order data analysis.
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