AzeroS: Extending LLM to Speech with Self-Generated Instruction-Free Tuning
Abstract
Extending large language models (LLMs) to the speech domain has recently gained significant attention. A typical approach connects a pretrained LLM with an audio encoder through a projection module and trains the resulting model on large-scale, task-specific instruction-tuning datasets. However, curating such instruction-tuning data for specific requirements is time-consuming, and models trained in this manner often generalize poorly to unseen tasks. In this work, we first formulate that the strongest generalization of a speech-LLM is achieved when it is trained with Self-Generated Instruction-Free Tuning (SIFT), in which supervision signals are generated by a frozen LLM using textual representations of speech as input. Our proposed SIFT paradigm eliminates the need for collecting task-specific question-answer pairs and yields the theoretically best generalization to unseen tasks. Building upon this paradigm, we introduce AZeroS (Auden Zero-instruction-tuned Speech-LLM), which is trained on speech-text pairs derived from publicly available corpora, including approximately 25,000 hours of speech with ASR transcripts and 3,000 hours of speech with paralinguistic labels. Built upon Qwen2.5-7B-Instruct, the model updates only two lightweight projection modules (23.8 million parameters each), while keeping both the LLM and audio encoders frozen. Despite the minimal training cost and modest data scale, AZeroS achieves state-of-the-art performance on both semantic and paralinguistic benchmarks, including VoiceBench, AIR-Bench Foundation (Speech), and AIR-Bench Chat (Speech).
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of AZeroS, a lightweight speech-LLM that utilizes Self-Generated Instruction-Free Tuning (SIFT) to achieve state-of-the-art performance in semantic and paralinguistic understanding without relying on extensive instruction-tuning datasets. This innovative approach not only enhances efficiency but also broadens the potential applications of multimodal LLMs in real-world scenarios.
Methodology
The methodology presented in this paper introduces a novel Self-Generated Instruction-Free Tuning (SIFT) paradigm, which is a significant departure from traditional instruction tuning methods that rely on manually curated datasets. The authors argue that by leveraging a frozen LLM to generate supervision signals from textual representations of speech, they can achieve better generalization and performance on unseen tasks. The two-stage training process, where the model first aligns semantic information and then incorporates paralinguistic cues, is well-structured and effectively demonstrates the potential of the proposed approach. The lightweight architecture, updating only projection modules while keeping the LLM and audio encoders frozen, is a practical innovation that enhances efficiency.
Results & evaluation
The experiments are comprehensive, utilizing a substantial amount of data (approximately 25,000 hours of speech) and multiple benchmarks (VoiceBench and AIR-Bench) to evaluate both semantic and paralinguistic understanding. The results indicate that AZeroS achieves state-of-the-art performance, outperforming existing models, which validates the effectiveness of the proposed SIFT paradigm. The use of diverse datasets and thorough evaluation metrics strengthens the credibility of the findings.
Reproducibility
The paper provides a clear description of the model architecture, training procedures, and datasets used, which aids in reproducibility. The authors have made their code available on GitHub, enhancing the accessibility of their work for further research and validation.
Limitations
One limitation is the reliance on the performance of the frozen LLM, which may not generalize well if the underlying model is not sufficiently robust. Additionally, while the results are impressive, the paper does not explore the potential limitations of the SIFT approach in scenarios where the LLM may struggle to generate accurate supervision signals. The paper could also benefit from a more detailed discussion on the scalability of the method with larger datasets or more complex tasks.
Broader impact
The proposed method has significant implications for the development of multimodal models, particularly in applications involving speech recognition and understanding. By reducing the need for extensive task-specific instruction data, this approach could democratize access to advanced speech-LLM technologies, making them more accessible for various applications, including virtual assistants, customer service, and accessibility tools for individuals with disabilities. The main contribution of this paper is the introduction of AZeroS, a lightweight speech-LLM that utilizes Self-Generated Instruction-Free Tuning (SIFT) to achieve state-of-the-art performance in semantic and paralinguistic understanding without relying on extensive instruction-tuning datasets. This innovative approach not only enhances efficiency but also broadens the potential applications of multimodal LLMs in real-world scenarios.
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