LLM$\times$MapReduce: Simplified Long-Sequence Processing using Large Language Models

ACL ARR 2025 February Submission7588 Authors

Abstract:

We propose a training-free framework that enables large language models (LLMs) to effectively process long texts, using a divide-and-conquer strategy for 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 outputs to produce the final response. The main challenge for divide-and-conquer long text processing frameworks lies in the risk of losing essential long-range information due to document splitting, 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. Experiments demonstrate that LLM$\times$MapReduce outperforms representative open-source and commercial long-context LLMs and is compatible with several models.
Our framework can also function as a data synthesis engine, capable of generating high-quality long-alignment data using only short-context LLMs.

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: prompting, applications, fine-tuning

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Publicly available software and/or pre-trained models, Data resources

Languages Studied: English, Chinese

Submission Number: 7588