LLMtimesMapReduce: Simplified Long-Sequence Processing using Large
  Language Models
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 LLMtimesMapReduce 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 LLMtimesMapReduce can outperform
representative open-source and commercial long-context LLMs, and is applicable
to several different models.
670f4a2adb1a6bcfe856e62a
Zihan Zhou
670f4a2adb1a6bcfe856e62b
Chong Li
670f4a2adb1a6bcfe856e62c
Xinyi Chen
670f4a2adb1a6bcfe856e62d
Shuo Wang
claimed_verified
2024-10-16T09:43:19.030Z
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Shuo Wang
user
shuo-hf
670f4a2adb1a6bcfe856e62e
Yu Chao
670f4a2adb1a6bcfe856e62f
Zhili Li
670f4a2adb1a6bcfe856e630
Haoyu Wang
670f4a2adb1a6bcfe856e631
Rongqiao An
670f4a2adb1a6bcfe856e632
Qi Shi
670f4a2adb1a6bcfe856e633
Zhixing Tan
670f4a2adb1a6bcfe856e634
Xu Han
670f4a2adb1a6bcfe856e635
Xiaodong Shi
670f4a2adb1a6bcfe856e636
Zhiyuan Liu
claimed_verified
2025-06-02T07:56:53.296Z
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/avatars/79dca137583875191ea883b193b630b7.svg
Zhiyuan Liu
user
zibuyu9
670f4a2adb1a6bcfe856e637
Maosong Sun
LLMtimesMapReduce: Simplified Long-Sequence Processing using Large
  Language Models
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 LLMtimesMapReduce 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 LLMtimesMapReduce can outperform
representative open-source and commercial long-context LLMs, and is applicable
to several different models.
A divide-and-conquer framework addresses long-range information loss in large language models by splittingdocuments into chunks and using structured protocols and calibration mechanisms to improve accuracy and performance.
large language models LLMs divide-and-conquer document understanding chunks inter-chunk dependency inter-chunk conflict structured information protocol in-context confidence calibration long-context LLMs