Summary of Towards Llm-guided Efficient and Interpretable Multi-linear Tensor Network Rank Selection, by Giorgos Iacovides et al.
Towards LLM-guided Efficient and Interpretable Multi-linear Tensor Network Rank Selection
by Giorgos Iacovides, Wuyang Zhou, Danilo Mandic
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework leverages large language models (LLMs) to guide rank selection in tensor network models for higher-order data analysis. The approach utilizes LLMs’ intrinsic reasoning capabilities and domain knowledge to enhance interpretability of rank choices, optimizing the objective function. This enables users without specialized expertise to utilize tensor network decompositions and understand the underlying rationale. Experimental results on financial datasets demonstrate interpretable reasoning, strong generalization, and potential for self-enhancement over iterations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This framework uses large language models (LLMs) to help choose the best rank in a special kind of math problem called a tensor network model. The LLMs are good at understanding what’s important and can make decisions based on that. This makes it easier for people without special knowledge to use these math problems and understand why they’re making certain choices. The results show that this works well on financial data, is good at generalizing to new information, and can even get better over time. |
Keywords
» Artificial intelligence » Generalization » Objective function
