Instruction

Context

• Goal: Your goal is to cluster the input data into meaningful categories for the given use case.
• Data: The input data will be a list of technical or business documents summaries in XML format, including the following elements:
  • id: document index.
  • text: document summary.
• Use case: {use_case}

Requirements

Format

• Output clusters in XML format with each cluster as a `` element, containing the following sub-elements:
  • id: category number starting from 1 in an incremental manner.
  • name: category name should be within {cluster_name_length} words. It can be either verb phrase or noun phrase, whichever is more appropriate.
  • description: category description should be within {cluster_description_length} words. Here is an example of your output:

    category id
    category name
    category description

• Total number of categories should be no more than {max_num_clusters}.
• Output should be in English only.

Quality

• No overlap or contradiction among the categories.
• Name is a concise and clear label for the category. Use only phrases that are specific to each category and avoid those that are common to all categories.
• Description differentiates one category from another.
• Name and description can accurately and consistently classify new data points without ambiguity.
• Name and description are consistent with each other. Output clusters match the data as closely as possible, without missing important categories or adding unnecessary ones.
• Output clusters should strive to be orthogonal, providing solid coverage of the target domain.
• Output clusters serve the given use case well.
• Output clusters should be specific and meaningful. - - Do not invent categories that are not in the data.

Data

{data_xml}

Questions

Q1. Please generate a cluster table from the input data that meets the requirements.

Tips

• The cluster table should be a flat list of mutually exclusive categories. Sort them based on their semantic relatedness.
• Though you should aim for {max_num_clusters} categories, you can have fewer than {max_num_clusters} categories in the cluster table; but do not exceed the limit.
• Be specific about each category. Do not include vague categories such as "Other", "General", "Unclear", "Miscellaneous" or "Undefined" in the cluster table.
• You can ignore low quality or ambiguous data points.

Q2. Why did you cluster the data the way you did? Explain your reasoning within {explanation_length} words.

Provide your answers between the tags: your generated cluster table with no more than {max_num_clusters} categories, explanation of your reasoning process within {explanation_length} words.

Output