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第174行： − + + − + − + − + − + − + − + + − + − ''' from opening a through road or street for public use across said public park in the Park of The City of Riverton." (Emphasis supplied.)<br>+ − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − + − #creddump is free software: you can redistribute it and/or modify<br>+ − # it under the terms of the GNU General Public License as published by<br>+ − # the Free Software Foundation, either version 3 of the License, or<br>+ − # (at your option) any later version.<br>+ − #<br>+ − # creddump is distributed in the hope that it will be useful.+ − + − − − − − − − + − + − + − + − + − + − + − + − + − + − + − + − [[文件:Quanta exam.png|无|缩略图|500x500像素|关于知识量子的具体示例]]+ − − 比如图中展示的50号聚类得到的知识量子，便是专门负责对数字序号进行预测的知识。而100号聚类得到的知识量子，是对于代码中换行符号的预测。这些知识量子的重要性及使用频率可以非常不同。根据[[齐普夫定律]]，作者假设知识量子在语料中的分布服从[[幂律分布]]。所以这正是作者能够从知识量子理论中推出神经标度律的关键假设所在。
无编辑摘要
==知识量子理论==
==知识量子理论==
2023年，MIT的Tegmark团队借鉴量子的概念，提出了知识量子理论来诠释大语言模型的各种现象，包括神经标度律、涌现等<ref name=":quanta" />。作者们假设，知识是以知识量子（knowledge quanta）的形式一份一份存在在自然语言当中的。当大语言模型进行训练和学习时，就是在学一个个知识量子。下图给出的，是作者们通过对梯度进行聚类得到的知识量子的示例。
2023年，MIT的Tegmark团队借鉴量子的概念，提出了知识量子理论来诠释大语言模型的各种现象，包括神经标度律、涌现等<ref name=":quanta" />。作者们假设，知识是以知识量子（knowledge quanta）的形式一份一份存在在自然语言当中的。当大语言模型进行训练和学习时，就是在学一个个知识量子。下表给出的，是作者们通过对梯度进行聚类得到的知识量子的示例。
{| class="wikitable"
{| class="wikitable"
|+自动发现的大语言模型知识量子
|-
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! Cluster 50: 递增数列片段 !! Cluster 100: 换行预测片段
! Cluster 50: 递增数列片段 !! Cluster 100: 换行预测片段
|-
|-
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01-Mi Querencia (Simón Díaz)<br>
01-Mi Querencia (Simón Díaz)\n<br>
02-Tonada De Luna Llena (Simón Díaz)<br>
02-Tonada De Luna Llena (Simón Díaz)\n<br>
03-Sabana (José Salazar/Simón Díaz)<br>
03-Sabana (José Salazar/Simón Díaz)\n<br>
04-Caballo Viejo (Simón Díaz)<br>
04-Caballo Viejo (Simón Díaz)\n<br>
05-Todo Este Campo Es Mío (Simón Díaz)<br>
05-Todo Este Campo Es Mío (Simón Díaz)\n<br>
06-La Pena Del Becerrero (Simón Díaz)
06-La Pena Del Becerrero (Simón Díaz)\n<br>
'''07'''
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Pursuant to 5TH CIR. R. 47.5, the court has determined that this opinion should not be published and is not precedent except under the limited circumstances set forth in 5TH CIR.
Pursuant to 5TH CIR. R. 47.5, the court has determined\n that this opinion should not be published and is not precedent\n except under the limited circumstances set forth in 5TH CIR.'''\n'''
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...from opening a through road or street for public use across said public park in the Park of The City of Riverton." (Emphasis supplied.)\n<br>
Appealing from that order, the city asserts (1) plaintiffs have no standing or right to maintain the action; (2) that the proposed road was in an undedicated part of the park; (3) that the proposed road was an access road and not a through street or part of the city's street system.
Appealing from that order, the city asserts (1) plaintiffs have no standing or right to maintain the action; (2) that the proposed road was in an undedicated part of the park; (3) that the proposed road was an access road and not a through street or part of the city's street system.('''4'''
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TO PERFORM QUADRATIC REGRESSION<br>
TO PERFORM QUADRATIC REGRESSION\n<br>
ON THE TI84 GRAPHING CALCULATOR.<br>
ON THE TI84 GRAPHING CALCULATOR.\n<br>
DETERMINE HOW WELL THE REGRESSION MODEL FITS THE DATA.<br>
DETERMINE HOW WELL THE REGRESSION MODEL FITS THE DATA.\n<br>
AND THEN MAKE PREDICTIONS USING THE REGRESSION EQUATION.<br>
AND THEN MAKE PREDICTIONS USING THE REGRESSION EQUATION.\n<br>
IN STATISTICS, REGRESSION ANALYSIS INCLUDES<br>
IN STATISTICS, REGRESSION ANALYSIS INCLUDES\n<br>
ANY TECHNIQUES USED FOR MODELING
ANY TECHNIQUES USED FOR MODELING '''\n'''
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4. Introduction<br>
4. Introduction\n<br>
5. Chapter 1: What Is Trust?<br>
5. Chapter 1: What Is Trust?\n<br>
6. Chapter 2: Trust Brings Rest<br>
6. Chapter 2: Trust Brings Rest\n<br>
7. Chapter 3: Who Can I Trust?<br>
7. Chapter 3: Who Can I Trust?\n<br>
8. Chapter 4: The Folly of Self-Reliance<br>
8. Chapter 4: The Folly of Self-Reliance\n<br>
9. Chapter 5: Trust God and Do Good (Part 1)<br>
9. Chapter 5: Trust God and Do Good (Part 1)\n<br>
10. Chapter 6: Trust God and Do Good (Part 2)<br>
10. Chapter 6: Trust God and Do Good (Part 2)\n<br>
11. Chapter 7: At All Times<br>
11. Chapter 7: At All Times\n<br>
12. Chapter 8
12. Chapter '''8'''
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creddump is free software: you can redistribute it and/or modify\n<br>
it under the terms of the GNU General Public License as published by\n<br>
the Free Software Foundation, either version 3 of the License, or\n<br>
(at your option) any later version.\n<br>
\n<br>
creddump is distributed in the hope that it will be useful.'''\n'''
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The chosen sites were recorded as:<br>
The chosen sites were recorded as:0 = sound (n = 13);1 = first visible sign of noncavitated lesion seen only when the tooth is dried;2 = visible noncavitated lesion seen when wet and dry;3 = microcavitation in enamel;4 = noncavitated lesion extending into dentine seen as an undermining shadow;5 = small cavitated lesion with visible dentine (less than 50% of surface);'''6'''
0 = sound (n = 13)<br>
1 = first visible sign of noncavitated lesion seen only when the tooth is dried<br>
2 = visible noncavitated lesion seen when wet and dry<br>
3 = microcavitation in enamel<br>
4 = noncavitated lesion extending into dentine seen as an undermining shadow<br>
5 = small cavitated lesion with visible dentine (less than 50% of surface)
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/**<br>
/**\n<br>
Copyright (c) 2019. The Android Open Source Project<br>
Copyright (c) 2019. The Android Open Source Project\n<br>
<br>
\n<br>
Licensed under the Apache License, Version 2.0 (the "License");<br>
Licensed under the Apache License, Version 2.0 (the "License");\n<br>
you may not use this file except in compliance with the License.
you may not use this file except in compliance with the License.'''\n'''
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QCBlock List Msg = 0x0a<br>
QCBlock List Msg = 0x0a \n<br>
Get Latest Status Msg = 0x0b<br>
Get Latest Status Msg = 0x0b \n<br>
Latest Status Msg = 0x0c<br>
Latest Status Msg = 0x0c \n<br>
Prepare Block HashMsg = 0x0d<br>
Prepare Block HashMsg = 0x0d \n<br>
Get View Change Msg = 0x0e<br>
Get View Change Msg = 0x0e \n<br>
Ping Msg = 0x0f
Ping Msg = 0x0'''f'''
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children have a lack of maturity and an underdeveloped sense of responsibility, leading to recklessness, impulsivity, and heedless risk-taking... Second, children are more vulnerable to negative influences and outside pressures, including from their family and peers; they have limited control over their own environment...
children have a lack of maturity and an underdeveloped\n sense of responsibility, leading to recklessness, impul-\nsivity, and heedless risk-taking... Second, children\n are more vulnerable to negative influences and\n outside pressures, including from their family and\n peers; they have limited control over their own envi-'''\n'''
|}
|}
比如上表中展示的50号聚类得到的知识量子，便是专门负责对数字序号进行预测的知识。而100号聚类得到的知识量子，是对于代码中换行符号的预测。这些知识量子的重要性及使用频率可以非常不同。根据[[齐普夫定律]]，作者假设知识量子在语料中的分布服从[[幂律分布]]。所以这正是作者能够从知识量子理论中推出神经标度律的关键假设所在。
那么，知识量子理论能否用来解释[[涌现]]，以及建立起涌现和[[神经标度律]]之间的关联呢？如下图所示，交叉熵损失随着模型参数规模的增大会有不同的下降方式，一种是当参数规模达到某一阈值时损失突然大幅下降，便说明在这个任务上大模型出现涌现现象；另外则是损失平滑线性地下降，这便对应的是[[神经标度律]]。作者发现，当任务只和单个量子相关（Monogenic）的时候，损失的变化呈现为涌现；而如果任务和多个量子都相关（Polygenic），损失的变化趋势便呈现为神经标度律。按照这种分析，神经标度律其实是大量知识量子各自涌现结果的平均现象。
那么，知识量子理论能否用来解释[[涌现]]，以及建立起涌现和[[神经标度律]]之间的关联呢？如下图所示，交叉熵损失随着模型参数规模的增大会有不同的下降方式，一种是当参数规模达到某一阈值时损失突然大幅下降，便说明在这个任务上大模型出现涌现现象；另外则是损失平滑线性地下降，这便对应的是[[神经标度律]]。作者发现，当任务只和单个量子相关（Monogenic）的时候，损失的变化呈现为涌现；而如果任务和多个量子都相关（Polygenic），损失的变化趋势便呈现为神经标度律。按照这种分析，神经标度律其实是大量知识量子各自涌现结果的平均现象。