LLMxMapReduce Long Text Frame Processing Framework

The LLMxMapReduce framework is an innovative technology jointly proposed by Xiamen University, Peking University and other six institutions in 2024, which is designed to handle long text problems in large language models (LLMs). The relevant paper results are "LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models". This technology allows the model to process multiple fragments in parallel by splitting the long context into multiple fragments, extracting key information from different fragments, and then summarizing them into the final answer. The core advantage of the LLMxMapReduce framework lies in its structured communication protocol and context confidence calibration mechanism, which enables cross-fragment information to be processed more efficiently.

The introduction of the LLMxMapReduce framework breaks the memory limitation of large models and theoretically realizes the processing capability of "infinite length" context. This technology has a general enhancement effect on the long text capability of large models, and can maintain stable performance and reduce the score loss of long text when the text continues to grow.

In addition, the LLMxMapReduce framework shows strong versatility and has achieved excellent results when used in combination with Qwen2-72B and MiniCPM3. The principle of this technology is inspired by the MapReduce framework widely used in the field of big data, and makes full use of the concept of "divide and conquer" to avoid the limitations of large models when processing very long texts. In this way, LLMxMapReduce can effectively process long texts, avoid information loss or wrong conclusions caused by segmentation, and thus improve the accuracy of the final result.