Towards LLM-guided Efficient and Interpretable Multi-linear Tensor Network Rank Selection

TL;DR

This paper introduces a framework using large language models to guide tensor network rank selection, enhancing interpretability and optimization in higher-order data analysis, validated on financial datasets.

Contribution

It presents a novel LLM-guided approach for tensor rank selection, improving interpretability and usability for non-experts in tensor network models.

Findings

Demonstrates effective rank selection on financial datasets

Shows improved interpretability of tensor network models

Achieves strong generalization to unseen data

Abstract

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.