# AnTenA: Actionable and Explainable Tensor Analysis System with Large Language Models

arXiv: https://arxiv.org/abs/2606.28708 · HTML: https://mlsanity.com/paper/2606.28708/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 27 Jun 2026.

## Builds on (most cited first, 6 of 6)

- [Tensor Decomposition for Signal Processing and Machine Learning](https://mlsanity.com/paper/1607.01668/) (2016)
- [Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era](https://mlsanity.com/paper/2403.08946/) (2024)
- [LLMs for Explainable AI: A Comprehensive Survey](https://mlsanity.com/paper/2504.00125/) (2025)
- [LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models](https://mlsanity.com/paper/2406.09008/) (2024)
- [LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs](https://mlsanity.com/paper/2308.01157/) (2023)
- [Language Model as Visual Explainer](https://mlsanity.com/paper/2412.07802/) (2024)

## Abstract (arXiv)

Accurately explaining hidden patterns in multi-aspect data has typically been done by leveraging labels and/or accompanying auxiliary metadata. However, labels and auxiliary data may be inaccurate (e.g. nonstandard, inconsistent), insufficient (e.g. static tabular metadata for time-dependent recordings), or unavailable. % We propose fullmethod (method), which leverages the knowledge of large language models (LLMs) to explain the hidden patterns in human narratives. method uses task-agnostic and task-specific prompts to explain extracted co-clustered latent patterns from tensor decomposition. To evaluate these explanations, we test the LLMs on forward and backward inference tasks. % Our demo system is available at https://github.com/dawonahn/ECMLPKDDAnTenA.