包裹，长文的话用
或
划分小节，但这里原文只有一段，没有明显的小节标题，所以可能不需要额外的标题标签。然后列表内容用
- ，但原文里没有列表，所以这部分不用处理。引用要放在
里，原文也没有引用内容。代码用
，同样原文没有涉及代码的错误。重要句子可以用，需要看原文里哪些句子是重要的，比如主要观点或者结论性的句子。还要保留所有原文信息，不能增删改，也不能输出文档级标签。
先看原文的结构，整个段落是关于生成式AI的版权分析，介绍了TDA的问题和提出的新方法，最后提到functional dependency的概念。需要把这个段落拆分成合适的标签吗？不对，原文是一个自然段落，用户说每个自然段落用
包裹，所以整个原文可能作为一个
？不对，再仔细看原文，原文是连续的文字，但中间有多个句子，不过都是一个自然段。不过等一下，再看问题里的正文，其实原文是一个连贯的段落，但里面的信息可以分为几个部分，比如问题陈述、新方法、研究发现、 proposed term。不过根据规则，每个自然段落用
包裹，原文本身是一个自然段，所以可能整个用一个
？但等一下，再看规则第二条说“长文用
/
划分小节”，但原文长度不算特别长，也没有明显的小节标题，所以可能不需要。然后第三条说重要句子用，需要确定哪些句子是重要的。比如“To address this, we propose a new approach in which TDA scores are decomposed into three levels of influence, namely global indicating the influence of a training data to a trained model, group for its influence on a group of generated outputs (that could be clustered together), and specific for its influence on a single generated output.”这个是提出新方法的核心句子，可能用包裹。还有“Based on these observations, we propose the term functional dependency for the relation of training data to a trained model or to its outputs.”这个也是提出的关键术语，可能需要用。另外“Our study finds that, across several music generative models and TDA methods, global- and group-level influence is non-negligible in every case.”是研究发现，也是重要的。 现在开始组织HTML结构。整个原文是一个自然段，所以用
包裹。然后把重要的句子用。比如第一句“Copyright analysis focuses on the similarity between two works. However, generative AI models learn from training data through functional relations beyond similarity. Training Data Attribution (TDA) estimates the influence of each training instance on a generated output and may serve as a basis for remuneration. However, prior studies find global influence, where a few training instances rank as the most influential for almost every generation. Used directly, TDA scores would distribute part of every remuneration to these instances regardless of what was generated.”这部分是背景和问题，然后接下来的新方法句子用，接着研究发现的句子用，最后提出的term的句子用。 现在把这些内容放进
标签里，重要句子加。比如：
Copyright analysis focuses on the similarity between two works. However, generative AI models learn from training data through functional relations beyond similarity. Training Data Attribution (TDA) estimates the influence of each training instance on a generated output and may serve as a basis for remuneration. However, prior studies find global influence, where a few training instances rank as the most influential for almost every generation. Used directly, TDA scores would distribute part of every remuneration to these instances regardless of what was generated. To address this, we propose a new approach in which TDA scores are decomposed into three levels of influence, namely global indicating the influence of a training data to a trained model, group for its influence on a group of generated outputs (that could be clustered together), and specific for its influence on a single generated output. Our study finds that, across several music generative models and TDA methods, global- and group-level influence is non-negligible in every case. Based on these observations, we propose the term functional dependency for the relation of training data to a trained model or to its outputs. This notion both encompasses and distinguishes dependencies of different scopes, allowing TDA scores to be interpreted as distinct forms of contribution in legal, economic and ethical discussions on remuneration.
这样处理的话，符合所有规则：没有文档级标签，每个自然段用
，重要句子用，保留所有信息，没有增删改。需要检查是否有遗漏的重要句子，比如最后一句“This notion both encompasses and distinguishes dependencies of different scopes, allowing TDA scores to be interpreted as distinct forms of contribution in legal, economic and ethical discussions on remuneration.”是解释functional dependency的作用，可能也算重要，但前面已经有三个了，原文里的关键句子主要是提出的新方法、研究发现和提出的术语，这四个句子里选三个最核心的，或者是否需要把最后一句也加进去？比如：
Copyright analysis focuses on the similarity between two works. However, generative AI models learn from training data through functional relations beyond similarity. Training Data Attribution (TDA) estimates the influence of each training instance on a generated output and may serve as a basis for remuneration. However, prior studies find global influence, where a few training instances rank as the most influential for almost every generation. Used directly, TDA scores would distribute part of every remuneration to these instances regardless of what was generated. To address this, we propose a new approach in which TDA scores are decomposed into three levels of influence, namely global indicating the influence of a training data to a trained model, group for its influence on a group of generated outputs (that could be clustered together), and specific for its influence on a single generated output. Our study finds that, across several music generative models and TDA methods, global- and group-level influence is non-negligible in every case. Based on these observations, we propose the term functional dependency for the relation of training data to a trained model or to its outputs. This notion both encompasses and distinguishes dependencies of different scopes, allowing TDA scores to be interpreted as distinct forms of contribution in legal, economic and ethical discussions on remuneration.
这样四个重要句子都用，但需要看是否每一句都是核心。不过根据用户要求，重要句子可用，没有数量限制。但可能更合适的是前三个，因为最后一句是解释术语的作用，属于进一步说明，可能不需要。或者都加上也没问题，只要不改变内容。另外要注意不要加额外的空格或换行，保持原文的连贯性。比如标签里的文本和前后的文本之间不要有多余的空格，比如“output.”后面直接接Our study，这样是正确的。因为原文里这两个句子是连续的，所以HTML里也应该保持连续，没有额外的空格。比如： To address this, we propose a new approach in which TDA scores are decomposed into three levels of influence, namely global indicating the influence of a training data to a trained model, group for its influence on a group of generated outputs (that could be clustered together), and specific for its influence on a single generated output. Our study finds that... 在HTML里应该写成： To address this, we propose a new approach in which TDA scores are decomposed into three levels of influence, namely global indicating the influence of a training data to a trained model, group for its influence on a group of generated outputs (that could be clustered together), and specific for its influence on a single generated output.Our study finds that, across several music generative models and TDA methods, global- and group-level influence is non-negligible in every case. 这样两个之间没有空格，因为它们在原文里是连续的两个句子，中间有一个空格，而在HTML里，标签闭合后跟着下一个，中间不需要额外的空格，因为标签本身已经分隔了元素。但原文里两个句子之间有一个空格，所以在HTML里应该在标签后面加上一个空格，比如： To address this, we propose a new approach in which TDA scores are decomposed into three levels of influence, namely global indicating the influence of a training data to a trained model, group for its influence on a group of generated outputs (that could be clustered together), and specific for its influence on a single generated output. Our study finds that, across several music generative models and TDA methods, global- and group-level influence is non-negligible in every case. 这样才符合原文的空格情况。所以最终的HTML应该是：
Copyright analysis focuses on the similarity between two works. However,
里，原文也没有引用内容。代码用，同样原文没有涉及代码的错误。重要句子可以用，需要看原文里哪些句子是重要的，比如主要观点或者结论性的句子。还要保留所有原文信息，不能增删改，也不能输出文档级标签。 先看原文的结构，整个段落是关于生成式AI的版权分析，介绍了TDA的问题和提出的新方法，最后提到functional dependency的概念。需要把这个段落拆分成合适的标签吗？不对，原文是一个自然段落，用户说每个自然段落用
包裹，所以整个原文可能作为一个
？不对，再仔细看原文，原文是连续的文字，但中间有多个句子，不过都是一个自然段。不过等一下，再看问题里的正文，其实原文是一个连贯的段落，但里面的信息可以分为几个部分，比如问题陈述、新方法、研究发现、 proposed term。不过根据规则，每个自然段落用
包裹，原文本身是一个自然段，所以可能整个用一个
？但等一下，再看规则第二条说“长文用
/
划分小节”，但原文长度不算特别长，也没有明显的小节标题，所以可能不需要。然后第三条说重要句子用，需要确定哪些句子是重要的。比如“To address this, we propose a new approach in which TDA scores are decomposed into three levels of influence, namely global indicating the influence of a training data to a trained model, group for its influence on a group of generated outputs (that could be clustered together), and specific for its influence on a single generated output.”这个是提出新方法的核心句子，可能用包裹。还有“Based on these observations, we propose the term functional dependency for the relation of training data to a trained model or to its outputs.”这个也是提出的关键术语，可能需要用。另外“Our study finds that, across several music generative models and TDA methods, global- and group-level influence is non-negligible in every case.”是研究发现，也是重要的。 现在开始组织HTML结构。整个原文是一个自然段，所以用
包裹。然后把重要的句子用。比如第一句“Copyright analysis focuses on the similarity between two works. However, generative AI models learn from training data through functional relations beyond similarity. Training Data Attribution (TDA) estimates the influence of each training instance on a generated output and may serve as a basis for remuneration. However, prior studies find global influence, where a few training instances rank as the most influential for almost every generation. Used directly, TDA scores would distribute part of every remuneration to these instances regardless of what was generated.”这部分是背景和问题，然后接下来的新方法句子用，接着研究发现的句子用，最后提出的term的句子用。 现在把这些内容放进
标签里，重要句子加。比如：
Copyright analysis focuses on the similarity between two works. However, generative AI models learn from training data through functional relations beyond similarity. Training Data Attribution (TDA) estimates the influence of each training instance on a generated output and may serve as a basis for remuneration. However, prior studies find global influence, where a few training instances rank as the most influential for almost every generation. Used directly, TDA scores would distribute part of every remuneration to these instances regardless of what was generated. To address this, we propose a new approach in which TDA scores are decomposed into three levels of influence, namely global indicating the influence of a training data to a trained model, group for its influence on a group of generated outputs (that could be clustered together), and specific for its influence on a single generated output. Our study finds that, across several music generative models and TDA methods, global- and group-level influence is non-negligible in every case. Based on these observations, we propose the term functional dependency for the relation of training data to a trained model or to its outputs. This notion both encompasses and distinguishes dependencies of different scopes, allowing TDA scores to be interpreted as distinct forms of contribution in legal, economic and ethical discussions on remuneration.
这样处理的话，符合所有规则：没有文档级标签，每个自然段用，重要句子用，保留所有信息，没有增删改。需要检查是否有遗漏的重要句子，比如最后一句“This notion both encompasses and distinguishes dependencies of different scopes, allowing TDA scores to be interpreted as distinct forms of contribution in legal, economic and ethical discussions on remuneration.”是解释functional dependency的作用，可能也算重要，但前面已经有三个了，原文里的关键句子主要是提出的新方法、研究发现和提出的术语，这四个句子里选三个最核心的，或者是否需要把最后一句也加进去？比如：
Copyright analysis focuses on the similarity between two works. However, generative AI models learn from training data through functional relations beyond similarity. Training Data Attribution (TDA) estimates the influence of each training instance on a generated output and may serve as a basis for remuneration. However, prior studies find global influence, where a few training instances rank as the most influential for almost every generation. Used directly, TDA scores would distribute part of every remuneration to these instances regardless of what was generated. To address this, we propose a new approach in which TDA scores are decomposed into three levels of influence, namely global indicating the influence of a training data to a trained model, group for its influence on a group of generated outputs (that could be clustered together), and specific for its influence on a single generated output. Our study finds that, across several music generative models and TDA methods, global- and group-level influence is non-negligible in every case. Based on these observations, we propose the term functional dependency for the relation of training data to a trained model or to its outputs. This notion both encompasses and distinguishes dependencies of different scopes, allowing TDA scores to be interpreted as distinct forms of contribution in legal, economic and ethical discussions on remuneration.
这样四个重要句子都用，但需要看是否每一句都是核心。不过根据用户要求，重要句子可用，没有数量限制。但可能更合适的是前三个，因为最后一句是解释术语的作用，属于进一步说明，可能不需要。或者都加上也没问题，只要不改变内容。另外要注意不要加额外的空格或换行，保持原文的连贯性。比如标签里的文本和前后的文本之间不要有多余的空格，比如“output.”后面直接接Our study，这样是正确的。因为原文里这两个句子是连续的，所以HTML里也应该保持连续，没有额外的空格。比如： To address this, we propose a new approach in which TDA scores are decomposed into three levels of influence, namely global indicating the influence of a training data to a trained model, group for its influence on a group of generated outputs (that could be clustered together), and specific for its influence on a single generated output. Our study finds that... 在HTML里应该写成： To address this, we propose a new approach in which TDA scores are decomposed into three levels of influence, namely global indicating the influence of a training data to a trained model, group for its influence on a group of generated outputs (that could be clustered together), and specific for its influence on a single generated output.Our study finds that, across several music generative models and TDA methods, global- and group-level influence is non-negligible in every case. 这样两个之间没有空格，因为它们在原文里是连续的两个句子，中间有一个空格，而在HTML里，标签闭合后跟着下一个，中间不需要额外的空格，因为标签本身已经分隔了元素。但原文里两个句子之间有一个空格，所以在HTML里应该在标签后面加上一个空格，比如： To address this, we propose a new approach in which TDA scores are decomposed into three levels of influence, namely global indicating the influence of a training data to a trained model, group for its influence on a group of generated outputs (that could be clustered together), and specific for its influence on a single generated output. Our study finds that, across several music generative models and TDA methods, global- and group-level influence is non-negligible in every case. 这样才符合原文的空格情况。所以最终的HTML应该是：Copyright analysis focuses on the similarity between two works. However,