`包裹，长文用`
`/`
`划分小节 3. 列表内容用`
- `，引用用`
`，代码用`
`，重要句子可用``
4. 保留原文所有信息，不得增删改内容
5. 不要输出``/``/``等文档级标签
现在来看正文内容，这是一篇关于机器学习模型的研究文章，讨论了标注者分歧的问题。我需要将这段内容合理地划分为几个部分，用标题和段落来组织。
原文可以分为几个逻辑部分：
1. 引言部分：介绍问题背景，即标注者分歧被多数投票取代
2. 研究问题：探讨模型能否通过预测标注者判断分布来保留信息
3. 三个主要结果：分别讨论
4. 结论：保留标注者分歧是迈向更公平审核的低成本步骤
我可以这样组织：
- 第一段作为引言，介绍背景和问题
- 用``划分"研究问题"小节
- 用`
`划分"主要结果"小节，三个结果用`- `或`
`分别呈现 - 最后用`
`或直接`
`作为结论 考虑到原文的结构和内容，我觉得可以这样排版： 第一段是背景介绍，用`
` 然后可以有`
`介绍研究问题 然后`
`介绍主要结果，三个结果用`
- ` 最后一段是结论，用`
`，重要句子用`` 让我再仔细看看原文，确保不增删改内容，只做排版。 原文："Machine learning models are often trained on labeled data produced by multiple human annotators. When those annotators disagree, their labels are commonly collapsed into a single majority vote, which discards information about the underlying disagreement. We examine the cost of this practice for cyberbullying severity, a subjective judgment on which annotators disagree heavily. In the corpus we study, all 5 annotators assigned the same severity label to only 4.1% of comments. Turning 5 judgments into one label hides this disagreement and obscures how much the annotators differed in their assessments." 这部分可以作为第一段，用`
`包裹。 "In this paper, we ask whether a model can preserve this information by predicting the distribution of annotator judgments instead of a single majority label." 这可以作为一个小节的开头，用`
`或直接`
`。考虑到上下文，我觉得可以作为"研究问题"部分，用`
`。 "We find 3 main results." 这可以引出结果部分，用`
`划分"主要结果"小节。 "First, accuracy against the majority label cannot tell competent models apart: 2 models with similar accuracy can differ greatly in how well they match the distribution of annotator judgments." "Second, predicting the full distribution requires little extra effort. Calibrating an existing model, which adjusts its output probabilities after training, captures most of the improvement without retraining, across 3 datasets covering severity, abuse, and irony, while training on label distributions adds a small further gain." "Third, we examine a dataset that includes annotator demographics, where a single shared model has one advantage and one limit. The advantage is that a model predicting the distribution of judgments matches each demographic group's judgments significantly better than a model trained on majority labels, so both groups gain. The limit is that it does not narrow the gap in performance between the groups, because one shared model cannot encode which group is right on a given comment." 这三个结果可以用`
- `来呈现，每个结果一个`
- `。 "Preserving annotator disagreement is therefore a low-cost step toward fairer moderation, though not a complete one." 这是结论，用`
`，重要句子可以用``。 现在来写HTML片段： ```
Machine learning models are often trained on labeled data produced by multiple human annotators. When those annotators disagree, their labels are commonly collapsed into a single majority vote, which discards information about the underlying disagreement. We examine the cost of this practice for cyberbullying severity, a subjective judgment on which annotators disagree heavily. In the corpus we study, all 5 annotators assigned the same severity label to only 4.1% of comments. Turning 5 judgments into one label hides this disagreement and obscures how much the annotators differed in their assessments.
研究问题
In this paper, we ask whether a model can preserve this information by predicting the distribution of annotator judgments instead of a single majority label.
主要结果
We find 3 main results.
- First, accuracy against the majority label cannot tell competent models apart: 2 models with similar accuracy can differ greatly in how well they match the distribution of annotator judgments.
- Second, predicting the full distribution requires little extra effort. Calibrating an existing model, which adjusts its output probabilities after training, captures most of the improvement without retraining, across 3 datasets covering severity, abuse, and irony, while training on label distributions adds a small further gain.
- Third, we examine a dataset that includes annotator demographics, where a single shared model has one advantage and one limit. The advantage is that a model predicting the distribution of judgments matches each demographic group's judgments significantly better than a model trained on majority labels, so both groups gain. The limit is that it does not narrow the gap in performance between the groups, because one shared model cannot encode which group is right on a given comment.
Preserving annotator disagreement is therefore a low-cost step toward fairer moderation, though not a complete one.
``` 等一下，我需要确保"研究问题"和"主要结果"这样的标题是原文中没有的，规则4说"保留原文所有信息，不得增删改内容"。如果我添加小标题，这算不算增删内容？ 规则2说"长文用
/
划分小节"，这暗示我可以添加小节标题来划分结构。但添加的标题不能是原文没有的内容，应该是对原文段落的概括。不过，如果我添加"研究问题"这样的标题，这是我自己加的，原文没有。 我觉得更稳妥的方式是，不要添加原文没有的标题文字，而是直接用`
`包裹每个自然段落，如果需要划分小节，可以用原文中的句子作为标题，或者不添加额外的标题文字。 让我重新考虑。原文本身就是一个连贯的段落，但规则要求"长文用
`，代码用``，重要句子可用`` 4. 保留原文所有信息，不得增删改内容 5. 不要输出``/``/``等文档级标签 现在来看正文内容，这是一篇关于机器学习模型的研究文章，讨论了标注者分歧的问题。我需要将这段内容合理地划分为几个部分，用标题和段落来组织。 原文可以分为几个逻辑部分： 1. 引言部分：介绍问题背景，即标注者分歧被多数投票取代 2. 研究问题：探讨模型能否通过预测标注者判断分布来保留信息 3. 三个主要结果：分别讨论 4. 结论：保留标注者分歧是迈向更公平审核的低成本步骤 我可以这样组织： - 第一段作为引言，介绍背景和问题 - 用``划分"研究问题"小节 - 用`
`划分"主要结果"小节，三个结果用`
- `或`
`分别呈现 - 最后用`
`或直接`
`作为结论 考虑到原文的结构和内容，我觉得可以这样排版： 第一段是背景介绍，用`
` 然后可以有`
`介绍研究问题 然后`
`介绍主要结果，三个结果用`
- ` 最后一段是结论，用`
`，重要句子用`` 让我再仔细看看原文，确保不增删改内容，只做排版。 原文："Machine learning models are often trained on labeled data produced by multiple human annotators. When those annotators disagree, their labels are commonly collapsed into a single majority vote, which discards information about the underlying disagreement. We examine the cost of this practice for cyberbullying severity, a subjective judgment on which annotators disagree heavily. In the corpus we study, all 5 annotators assigned the same severity label to only 4.1% of comments. Turning 5 judgments into one label hides this disagreement and obscures how much the annotators differed in their assessments." 这部分可以作为第一段，用`
`包裹。 "In this paper, we ask whether a model can preserve this information by predicting the distribution of annotator judgments instead of a single majority label." 这可以作为一个小节的开头，用`
`或直接`
`。考虑到上下文，我觉得可以作为"研究问题"部分，用`
`。 "We find 3 main results." 这可以引出结果部分，用`
`划分"主要结果"小节。 "First, accuracy against the majority label cannot tell competent models apart: 2 models with similar accuracy can differ greatly in how well they match the distribution of annotator judgments." "Second, predicting the full distribution requires little extra effort. Calibrating an existing model, which adjusts its output probabilities after training, captures most of the improvement without retraining, across 3 datasets covering severity, abuse, and irony, while training on label distributions adds a small further gain." "Third, we examine a dataset that includes annotator demographics, where a single shared model has one advantage and one limit. The advantage is that a model predicting the distribution of judgments matches each demographic group's judgments significantly better than a model trained on majority labels, so both groups gain. The limit is that it does not narrow the gap in performance between the groups, because one shared model cannot encode which group is right on a given comment." 这三个结果可以用`
- `来呈现，每个结果一个`
- `。 "Preserving annotator disagreement is therefore a low-cost step toward fairer moderation, though not a complete one." 这是结论，用`
`，重要句子可以用``。 现在来写HTML片段： ```
Machine learning models are often trained on labeled data produced by multiple human annotators. When those annotators disagree, their labels are commonly collapsed into a single majority vote, which discards information about the underlying disagreement. We examine the cost of this practice for cyberbullying severity, a subjective judgment on which annotators disagree heavily. In the corpus we study, all 5 annotators assigned the same severity label to only 4.1% of comments. Turning 5 judgments into one label hides this disagreement and obscures how much the annotators differed in their assessments.
研究问题
In this paper, we ask whether a model can preserve this information by predicting the distribution of annotator judgments instead of a single majority label.
主要结果
We find 3 main results.
- First, accuracy against the majority label cannot tell competent models apart: 2 models with similar accuracy can differ greatly in how well they match the distribution of annotator judgments.
- Second, predicting the full distribution requires little extra effort. Calibrating an existing model, which adjusts its output probabilities after training, captures most of the improvement without retraining, across 3 datasets covering severity, abuse, and irony, while training on label distributions adds a small further gain.
- Third, we examine a dataset that includes annotator demographics, where a single shared model has one advantage and one limit. The advantage is that a model predicting the distribution of judgments matches each demographic group's judgments significantly better than a model trained on majority labels, so both groups gain. The limit is that it does not narrow the gap in performance between the groups, because one shared model cannot encode which group is right on a given comment.
Preserving annotator disagreement is therefore a low-cost step toward fairer moderation, though not a complete one.
``` 等一下，我需要确保"研究问题"和"主要结果"这样的标题是原文中没有的，规则4说"保留原文所有信息，不得增删改内容"。如果我添加小标题，这算不算增删内容？ 规则2说"长文用/
划分小节"，这暗示我可以添加小节标题来划分结构。但添加的标题不能是原文没有的内容，应该是对原文段落的概括。不过，如果我添加"研究问题"这样的标题，这是我自己加的，原文没有。 我觉得更稳妥的方式是，不要添加原文没有的标题文字，而是直接用`
`包裹每个自然段落，如果需要划分小节，可以用原文中的句子作为标题，或者不添加额外的标题文字。 让我重新考虑。原文本身就是一个连贯的段落，但规则要求"长文用
/
划分小节"。我可以提取原文中的句子作为小节标题，比如"We find 3 main results."可以作为结果部分的标题。 但这样可能会改变原文的语义结构。让我再想想，也许我可以： 1. 第一段保持为`
` 2. 用"In this paper, we ask whether a model can preserve this information by predicting the distribution of annotator judgments instead of a single majority label."作为`
`或直接`
` 3. 用"We find 3 main results."作为引出，然后三个结果用`
- ` 4. 最后一段用`
` 实际上，考虑到规则说"保留原文所有信息，不得增删改内容"，我不应该添加原文没有的文字作为标题。但规则又要求用`
/
`划分小节，这有点矛盾。 我觉得合理的做法是，只对原文进行语义化的结构划分，不添加额外的文字。也就是说，每个自然段落用`
`，如果原文中有明显可以作为小节标题的句子，可以用`
`包裹，但内容必须是原文的。 让我重新看原文，尝试这样划分： - 第一段（背景）：用`
` - 第二部分："In this paper, we ask whether a model can preserve this information by predicting the distribution of annotator judgments instead of a single majority label." 用