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

Researchers propose AnTenA, a system that uses large language models to explain hidden patterns in multi-aspect data without relying on potentially inaccurate labels or metadata. The system employs tensor decomposition and LLM prompts for explanation, evaluated through forward and backward inference tasks.

arXiv:2606.28708v1 Announce Type: new Abstract: 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/ECML PKDD AnTenA.