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. Across two LLM families and three scales (1-8B), we show that decoder redundancy is largely inherited from the pretrained LLM: text and speech inputs yield similar redundant blocks. We then measure excess capacity by pruning decoder layers and analysing post-pruning healing to increase robustness. Our findings show t
Ongoing research into LLM efficiency and architecture optimization is a critical bottleneck for scaling AI. This paper addresses a key area of redundancy in SpeechLLMs as a direct extension of that effort.
Understanding and reducing redundancy in large language models, especially those integrated with speech, can significantly lower inference costs and computational requirements, making advanced AI more accessible and energy efficient.
The focus shifts towards more efficient and pruned LLM architectures, potentially lowering the computational barrier for deployment and accelerating development cycles due to reduced resource needs.
- · AI compute providers (more efficient usage)
- · LLM developers (reduced model sizes/costs)
- · Cloud service providers (lower inference costs)
- · Manufacturers of oversized AI hardware (if efficiency gains mean less need for r
More efficient SpeechLLMs enable broader deployment in resource-constrained environments.
Reduced computational demands for advanced AI models could accelerate AI adoption across various industries, including edge devices.
Increased efficiency could free up compute resources, potentially impacting the demand curve for new silicon and energy, if not entirely offset by increased overall AI usage.
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Read at arXiv cs.AI