SDTP addresses the computational bottleneck of LLMs when processing long sequences by intelligently identifying and removing less important tokens during inference.

• Leverages feature attribution theory to determine token importance
• Implements a dynamic pruning strategy that adapts throughout the inference process
• Significantly reduces computational costs while maintaining output quality
• Enables more efficient processing of long-context scenarios

This engineering innovation makes LLMs more practical for real-world applications by addressing one of their fundamental limitations: the quadratic complexity of attention mechanisms when handling long inputs.

Saliency-driven Dynamic Token Pruning for Large Language Models