title: LLMtimesMapReduce: Simplified Long-Sequence Processing using Large
    Language Models
summary: 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.
paper_authors._id: 670f4a2adb1a6bcfe856e62a
paper_authors.name: Zihan Zhou
paper_authors._id: 670f4a2adb1a6bcfe856e62b
paper_authors.name: Chong Li
paper_authors._id: 670f4a2adb1a6bcfe856e62c
paper_authors.name: Xinyi Chen
paper_authors._id: 670f4a2adb1a6bcfe856e62d
paper_authors.name: Shuo Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-16T09:43:19.030Z
paper_authors._id: 648183b3ec65b8b77d84693a
paper_authors.avatarUrl: /avatars/a3474e8839771a084e88522c6985d6db.svg
paper_authors.fullname: Shuo Wang
paper_authors.type: user
paper_authors.user: shuo-hf
paper_authors._id: 670f4a2adb1a6bcfe856e62e
paper_authors.name: Yu Chao
paper_authors._id: 670f4a2adb1a6bcfe856e62f
paper_authors.name: Zhili Li
paper_authors._id: 670f4a2adb1a6bcfe856e630
paper_authors.name: Haoyu Wang
paper_authors._id: 670f4a2adb1a6bcfe856e631
paper_authors.name: Rongqiao An
paper_authors._id: 670f4a2adb1a6bcfe856e632
paper_authors.name: Qi Shi
paper_authors._id: 670f4a2adb1a6bcfe856e633
paper_authors.name: Zhixing Tan
paper_authors._id: 670f4a2adb1a6bcfe856e634
paper_authors.name: Xu Han
paper_authors._id: 670f4a2adb1a6bcfe856e635
paper_authors.name: Xiaodong Shi
paper_authors._id: 670f4a2adb1a6bcfe856e636
paper_authors.name: Zhiyuan Liu
paper_authors._id: 670f4a2adb1a6bcfe856e637
paper_authors.name: Maosong Sun
paper_title: LLMtimesMapReduce: Simplified Long-Sequence Processing using Large
    Language Models
paper_summary: 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.
paper_ai_keywords: LLM training-free framework divide-and-conquer strategy document understanding LLM$\times$MapReduce framework intermediate answers long-range information inter-chunk dependency inter-chunk conflict structured information protocol in-context confidence calibration mechanism long-context LLMs