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Sentence-Anchored Gist Compression for Long-Context LLMs

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The study titled 'Sentence-Anchored Gist Compression for Long-Context LLMs' presents a novel approach to context compression in Large Language Models (LLMs), particularly focusing on the 3-billion-parameter LLaMA model. By utilizing learned compression tokens, the research shows that LLMs can effectively reduce their context size by factors ranging from 2x to 8x while maintaining performance levels comparable to existing compression techniques. This is particularly significant as the demand for processing long sequences in AI continues to rise, necessitating more efficient models. The findings suggest that fine-tuning pre-trained LLMs can lead to substantial improvements in memory and computational efficiency, which is essential for advancing AI capabilities.
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