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Naive Bayes-based Context Extension

使用朴素贝叶斯思想来扩展LLM的Context处理长度。

现在，任何LLM都可以利用NBCE成为可以处理任意长Context的模型了（只要算力足够）！

简介

基于朴素贝叶斯所启发的公式：

细节请看博客：https://kexue.fm/archives/9617

所给的Demo包含12段不同的Context，总长度为9000多字，连同8个问题一次性输入到模型中（测试模型训练长度为2048，参数量为7B，可以在OpenBuddy下载），模型能够逐一根据所给Context正确回答这8个问题。值得指出的是，所有的Context、问题和答案加起来，超过了1万字！另外，有朋友简单尝试了简历匹配和作文打分应用，效果也尚可，非常建议大家亲自调试一下。

最新测试结果：在8*A800下，7B模型可以处理50k的context，并能正确地做阅读理解。(没有用完所有GPU，大概消耗160G显存）

特点

即插即用
模型无关
不用微调
线性效率
实现简单
效果尚可
可解释性

引用

@inproceedings{nbce_naacl,
  author       = {Jianlin Su and
                  Murtadha Ahmed and
                  Bo Wen and
                  Luo Ao and
                  Mingren Zhu and
                  Yunfeng Liu},
  editor       = {Kevin Duh and
                  Helena G{\'{o}}mez{-}Adorno and
                  Steven Bethard},
  title        = {Naive Bayes-based Context Extension for Large Language Models},
  booktitle    = {Proceedings of the 2024 Conference of the North American Chapter of
                  the Association for Computational Linguistics: Human Language Technologies
                  (Volume 1: Long Papers), {NAACL} 2024, Mexico City, Mexico, June 16-21,
                  2024},
  pages        = {7791--7807},
  publisher    = {Association for Computational Linguistics},
  year         = {2024},
  url          = {https://doi.org/10.18653/v1/2024.naacl-long.431},
  doi          = {10.18653/V1/2024.NAACL-LONG.431},
  timestamp    = {Thu, 29 Aug 2024 17:13:57 +0200},
}

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