Dynamic Positional Attention Modulation for Parameter-Efficient Fine-Tuning of Large Language Models

Key points

• Parameter-efficient fine-tuning (PEFT) has become a standard approach for adapting large language models to downstream tasks.1
• In practice, attention representations exhibit non-uniform behavior, and positional encoding mechanisms such as rotary positional embeddings (RoPE) induce dimension-dependent positional structure, making uniform adaptation suboptimal.1
• DyPAM combines input-conditioned, dimension-wise modulation with head-wise and layer-wise structural modulation, performing fine-grained adaptation of positional attention aligned with the RoPE-induced structure without modifying the pretrained backbone.1
• Extensive experiments on mathematical and commonsense reasoning benchmarks across multiple backbone models demonstrate that DyPAM consistently outperforms existing strong PEFT baselines.1

Sources (1)

• [1]Dynamic Positional Attention Modulation for Parameter-Efficient Fine-Tuning of Large Language Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 06:55 AM

  In this work, we propose DyPAM (Dynamic Positional Attention Modulation), a PEFT method that adapts how positional information contributes to attention by operating directly on the query and key representations.

  Parameter-efficient fine-tuning (PEFT) has become a standard approach for adapting large language models to downstream tasks.

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