AnTenA: Actionable and Explainable Tensor Analysis System with Large Language Models
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Computer Science > Computation and Language
Title:AnTenA: Actionable and Explainable Tensor Analysis System with Large Language Models
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 this https URL.
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.28708 [cs.CL] |
| (or arXiv:2606.28708v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.28708
arXiv-issued DOI via DataCite (pending registration)
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