LLM$\times$MapReduce: Simplified Long-Sequence Processing using Large Language Models
This addresses the challenge of long-sequence processing for applications involving extremely long texts, with incremental improvements in handling inter-chunk dependencies and conflicts.
The paper tackles the problem of processing long texts with large language models by proposing a training-free divide-and-conquer framework that splits documents into chunks and aggregates answers, achieving performance that outperforms representative open-source and commercial long-context LLMs.
Enlarging the context window of large language models (LLMs) has become a crucial research area, particularly for applications involving extremely long texts. In this work, we propose a novel training-free framework for processing long texts, utilizing a divide-and-conquer strategy to achieve comprehensive document understanding. The proposed LLM$\times$MapReduce framework splits the entire document into several chunks for LLMs to read and then aggregates the intermediate answers to produce the final output. The main challenge for divide-and-conquer long text processing frameworks lies in the risk of losing essential long-range information when splitting the document, which can lead the model to produce incomplete or incorrect answers based on the segmented texts. Disrupted long-range information can be classified into two categories: inter-chunk dependency and inter-chunk conflict. We design a structured information protocol to better cope with inter-chunk dependency and an in-context confidence calibration mechanism to resolve inter-chunk conflicts. Experimental results demonstrate that LLM$\times$MapReduce can outperform representative open-source and commercial long-context LLMs, and is applicable to several different models.