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    <abstract>Large language models (LLMs) exhibit remarkable performance across diverse tasks, indicating their potential for expansion into large speech-text models (LSMs) by integrating speech capabilities. Although unified speech-text pre-training and multimodal data instruction-tuning offer considerable benefits, these methods generally entail significant resource demands and tend to overfit specific tasks.This study aims to refine the use of speech datasets for LSM training by addressing the limitations of vanilla instruction tuning. We explore the instruction-following dynamics within LSMs, identifying a critical issue termed speech anchor bias—a tendency for LSMs to over-rely on speech inputs, mistakenly interpreting the entire speech modality as directives, thereby neglecting textual instructions.To counteract this bias, we introduce a self-powered LSM that leverages augmented automatic speech recognition data generated by the model itself for more effective instruction tuning. Our experiments across a range of speech-based tasks demonstrate that self-powered LSM mitigates speech anchor bias and improves the fusion of speech and text modalities in LSMs. Data, code and scripts are freely available at https://github.com/ytf-philp/Self-powered-LSM.</abstract>
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%0 Conference Proceedings
%T Self-Powered LLM Modality Expansion for Large Speech-Text Models
%A Yu, Tengfei
%A Liu, Xuebo
%A Hou, Zhiyi
%A Ding, Liang
%A Tao, Dacheng
%A Zhang, Min
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F yu-etal-2024-self-powered
%X Large language models (LLMs) exhibit remarkable performance across diverse tasks, indicating their potential for expansion into large speech-text models (LSMs) by integrating speech capabilities. Although unified speech-text pre-training and multimodal data instruction-tuning offer considerable benefits, these methods generally entail significant resource demands and tend to overfit specific tasks.This study aims to refine the use of speech datasets for LSM training by addressing the limitations of vanilla instruction tuning. We explore the instruction-following dynamics within LSMs, identifying a critical issue termed speech anchor bias—a tendency for LSMs to over-rely on speech inputs, mistakenly interpreting the entire speech modality as directives, thereby neglecting textual instructions.To counteract this bias, we introduce a self-powered LSM that leverages augmented automatic speech recognition data generated by the model itself for more effective instruction tuning. Our experiments across a range of speech-based tasks demonstrate that self-powered LSM mitigates speech anchor bias and improves the fusion of speech and text modalities in LSMs. Data, code and scripts are freely available at https://github.com/ytf-philp/Self-powered-LSM.
%U https://aclanthology.org/2024.emnlp-main.690
%P 12401-12417

Markdown (Informal)

[Self-Powered LLM Modality Expansion for Large Speech-Text Models](https://aclanthology.org/2024.emnlp-main.690) (Yu et al., EMNLP 2024)

• Self-Powered LLM Modality Expansion for Large Speech-Text Models (Yu et al., EMNLP 2024)

ACL

• Tengfei Yu, Xuebo Liu, Zhiyi Hou, Liang Ding, Dacheng Tao, and Min Zhang. 2024. Self-Powered LLM Modality Expansion for Large Speech-Text Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 12401–12417, Miami, Florida, USA. Association for Computational Linguistics.