<ul><li>Developing recurrent sub-quadratic models to enhance long-context processing efficiency is a recent trend in large language models.</li><li>Experiments show that current models, despite being trained for extended contexts, underutilize long contexts due to fixed-size recurrent memory constraints.</li><li>A chunk-based inference method that focuses on relevant parts of input improves performance significantly for various long-context tasks.</li><li>This approach not only mitigates recurrent memory failures but also achieves state-of-the-art results in challenging benchmarks, questioning the true utilization of long-range dependencies in recurrent models.</li></ul>

Overflow Prevention Enhances Long-Context Recurrent LLMs

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