Protein language models show depth inefficiency similar to LLMs

Researchers have investigated the "Curse of Depth" phenomenon in protein language models (PLMs), finding that, similar to large language models, many layers in PLMs contribute minimally to final predictions. This study analyzed seven popular PLM families, examining autoregressive, masked, and diffusion objectives across various scales. The findings indicate that a significant portion of task-relevant computation is concentrated in specific layers, with others offering only incremental improvements, a trend observed even in multimodal PLMs. AI

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IMPACT Suggests potential for more efficient protein language model architectures and training methods.

RANK_REASON Academic paper analyzing a phenomenon in protein language models.

Read on arXiv cs.LG →

• arXiv
• Amir Mohammad Karimi Mamaghan
• Hugging Face

• paper
• model release