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SpeechLLM decoders show significant redundancy, allowing for layer pruning

By PulseAugur Editorial · [1 sources] ·

Researchers have investigated the redundancy within decoder layers of Speech Large Language Models (SpeechLLMs), which typically comprise over 90% of the model's parameters. Their study across various model sizes revealed that a significant portion of these decoder layers can be pruned without substantially impacting Automatic Speech Recognition (ASR) performance. Findings indicate that even 7-8B parameter models can retain good ASR capabilities with only 60% of their decoder layers intact, a trend observed across different scales and tasks, including speech translation. AI

IMPACT Suggests potential for more efficient SpeechLLM architectures, reducing computational costs and enabling wider deployment.

RANK_REASON Academic paper detailing research findings on model architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SpeechLLM decoders show significant redundancy, allowing for layer pruning

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Academic paper detailing research findings on model architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Adel Moumen, Guangzhi Sun, Philip C Woodland ·

    Measuring the Redundancy of Decoder Layers in SpeechLLMs

    arXiv:2603.05121v2 Announce Type: replace-cross Abstract: Speech Large Language Models route speech encoder representations into an LLM decoder that typically accounts for over 90% of total parameters. We study how much of this decoder capacity is actually needed for speech tasks…

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