# Adaptive Discovery of Interpretable Audio Attributes with Multimodal LLMs for Low-Resource Classification

arXiv: https://arxiv.org/abs/2603.06991 · HTML: https://mlsanity.com/paper/2603.06991/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 7 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [Binding Language Models in Symbolic Languages](https://mlsanity.com/paper/2210.02875/) (2022)
- [Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering](https://mlsanity.com/paper/2305.03403/) (2023)
- [The autofeat Python Library for Automated Feature Engineering and Selection](https://mlsanity.com/paper/1901.07329/) (2019)
- [ClusterLLM: Large Language Models as a Guide for Text Clustering](https://mlsanity.com/paper/2305.14871/) (2023)
- [Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning](https://mlsanity.com/paper/2406.08527/) (2024)
- [BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra](https://mlsanity.com/paper/2402.17882/) (2024)
- [Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection](https://mlsanity.com/paper/2305.12741/) (2023)
- [Summaries as Centroids for Interpretable and Scalable Text Clustering](https://mlsanity.com/paper/2502.09667/) (2025)

## Abstract (arXiv)

In predictive modeling for low-resource audio classification, extracting high-accuracy and interpretable attributes is critical. Particularly in high-reliability applications, interpretable audio attributes are indispensable. While human-driven attribute discovery is effective, its low throughput becomes a bottleneck. We propose a method for adaptively discovering interpretable audio attributes using Multimodal Large Language Models (MLLMs). By replacing humans in the AdaFlock framework with MLLMs, our method achieves significantly faster attribute discovery. Our method dynamically identifies salient acoustic characteristics via prompting and constructs an attribute-based ensemble classifier. Experimental results across various audio tasks demonstrate that our method outperforms direct MLLM prediction in the majority of evaluated cases. The entire training completes within 11 minutes, proving it a practical, adaptive solution that surpasses conventional human-reliant approaches.