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ALARM: Audio-Language Alignment for Reasoning Models

arXiv:2603.09556v1 Announce Type: new Abstract: Large audio language models (ALMs) extend LLMs with auditory understanding. A common approach freezes the LLM and trains only an adapter on self-generated targets. However, this fails for reasoning LLMs (RLMs) whose built-in chain-of-thought traces expose the textual surrogate input, yielding unnatural responses. We propose self-rephrasing, converting self-generated responses into audio-understanding variants compatible with RLMs while preserving distributional alignment. We further fuse and compress multiple audio encoders for stronger representations. For training, we construct a 6M-instance multi-task corpus (2.5M unique prompts) spanning 19K hours of speech, music, and sound. Our 4B-parameter ALM outperforms similarly sized models and surpasses most larger ALMs on related audio-reasoning benchmarks, while preserving textual capabilities with a low training cost. Notably, we achieve the best open-source result on the MMAU-speech and M

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Petr Grinberg, Hassan Shahmohammadi · · 1 min read · 18 views
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arXiv:2603.09556v1 Announce Type: new Abstract: Large audio language models (ALMs) extend LLMs with auditory understanding. A common approach freezes the LLM and trains only an adapter on self-generated targets. However, this fails for reasoning LLMs (RLMs) whose built-in chain-of-thought traces expose the textual surrogate input, yielding unnatural responses. We propose self-rephrasing, converting self-generated responses into audio-understanding variants compatible with RLMs while preserving distributional alignment. We further fuse and compress multiple audio encoders for stronger representations. For training, we construct a 6M-instance multi-task corpus (2.5M unique prompts) spanning 19K hours of speech, music, and sound. Our 4B-parameter ALM outperforms similarly sized models and surpasses most larger ALMs on related audio-reasoning benchmarks, while preserving textual capabilities with a low training cost. Notably, we achieve the best open-source result on the MMAU-speech and MMSU benchmarks and rank third among all the models.

Executive Summary

The article 'ALARM: Audio-Language Alignment for Reasoning Models' proposes a novel approach to training audio-language alignment models (ALMs) that extend large language models (LLMs) with auditory understanding. The authors develop a self-rephrasing method to convert self-generated responses into audio-understanding variants compatible with reasoning LLMs. The proposed method is tested on a 6M-instance multi-task corpus and achieves state-of-the-art results on several audio-reasoning benchmarks. While the article presents promising findings, its practical applications and policy implications require further exploration. The proposed method has the potential to revolutionize the field of audio-language understanding, but its limitations and challenges need to be addressed. This article is a significant contribution to the field of natural language processing and AI research.

Key Points

  • ▸ The authors propose a self-rephrasing method to align audio-understanding variants with reasoning LLMs.
  • ▸ The method is tested on a 6M-instance multi-task corpus and achieves state-of-the-art results on several audio-reasoning benchmarks.
  • ▸ The proposed approach has the potential to revolutionize the field of audio-language understanding.

Merits

Strength in Addressing Limitations of Previous Approaches

The authors' self-rephrasing method effectively addresses the limitations of previous approaches, which freeze the LLM and train only an adapter on self-generated targets. This innovative approach enables the development of more accurate and robust ALMs.

Demerits

Training Data Requirements

The proposed method requires a large multi-task corpus with 6M instances, which may be challenging to obtain or replicate in certain settings. This limitation may hinder the widespread adoption of the proposed approach.

Expert Commentary

The article 'ALARM: Audio-Language Alignment for Reasoning Models' presents a significant contribution to the field of natural language processing and AI research. The proposed self-rephrasing method effectively addresses the limitations of previous approaches and demonstrates promising results on several audio-reasoning benchmarks. However, the proposed method requires further exploration of its practical applications and policy implications. Additionally, the training data requirements and potential limitations of the proposed approach need to be carefully considered. Overall, this article is a valuable addition to the field of AI research and has the potential to revolutionize the development of more accurate and robust ALMs.

Recommendations

  • ✓ Future research should focus on developing more efficient and cost-effective methods for training ALMs with large multi-task corpora.
  • ✓ The proposed method should be tested on more diverse and challenging datasets to evaluate its robustness and generalizability.

Sources

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