Cross-attention layer placement impacts LLM instruction-following and security

Researchers have explored the impact of system-prompt anchoring using cross-attention layers in large language models. Their study, involving a 1.5B parameter backbone and a subsequent 8B scaling study, found that the placement of these cross-attention layer (CAL) blocks significantly affects performance and parameter efficiency, with later placements generally being more effective. The experiments indicate that while cross-attention alters instruction-following and security behaviors, it largely preserves general task performance. AI

IMPACT This research offers insights into optimizing LLM behavior by fine-tuning cross-attention layer placement, potentially improving instruction following and security.

RANK_REASON The cluster contains a research paper detailing novel methods for improving LLM performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX
• Cross-Attention Layer (CAL)
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv
• Lixing Li
• ScienceCast
• System-Prompt Anchoring

• paper
• model release

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