CodeTheory/first-dataset

---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---

# Dataset Card for first-dataset

This dataset has been created with [Argilla](https://docs.argilla.io).

As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).

## Dataset Description

- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

This dataset contains:

* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.

* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.

* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.

### Load with Argilla

To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:

```python
import argilla as rg

ds = rg.FeedbackDataset.from_huggingface("CodeTheory/first-dataset")
```

### Load with `datasets`

To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:

```python
from datasets import load_dataset

ds = load_dataset("CodeTheory/first-dataset")
```

### Supported Tasks and Leaderboards

This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).

There are no leaderboards associated with this dataset.

### Languages

[More Information Needed]

## Dataset Structure

### Data in Argilla

The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.

The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.

| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| info | info | text | True | True |
| question | question | text | True | True |
| l2 | L2 | text | True | True |
| gpt4 | GPT4 | text | True | True |
| glm4 | GLM4 | text | True | True |
| kimichat | KiMiChat | text | True | True |

The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.

| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| bed-case | 是否为Bad Case | label_selection | True | N/A | ['是', '否'] |
| follow-instructions | 是否指令遵循 | label_selection | False | N/A | ['是', '否'] |
| accurate-answer | 答案是否准确 | label_selection | False | N/A | ['是', '否'] |
| richness | 丰富度 | label_selection | False | N/A | ['好', '不好'] |
| completeness | 完整性 | label_selection | False | N/A | ['好', '不好'] |
| specification | 格式 | label_selection | False | N/A | ['好', '不好'] |
| synthesis | 整体 | label_selection | False | N/A | ['好', '不好'] |
| preference | 哪个回复最好？按照从好到坏的顺序进行排序 | ranking | False | N/A | ['L2', 'GPT4', 'GLM4', 'KiMiChat'] |
| suggestion | 建议 | text | False | N/A | N/A |

The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".

The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.

| Metadata Name | Title | Type | Values | Visible for Annotators |
| ------------- | ----- | ---- | ------ | ---------------------- |

The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.

### Data Instances

An example of a dataset instance in Argilla looks as follows:

```json
{
"external_id": null,
"fields": {
"glm4": "\u6807\u9898\uff1a\u3010\u7559\u5b66\u653b\u7565\u3011\u7559\u5b66\u751f\u7533\u8bf7\u5fc5\u5907\u5efa\u8bae\uff0c\u8ba9\u4f60\u5c11\u8d70\u5f2f\u8def\uff0c\u4e00\u8def\u7545\u901a\uff01\n\n\u5185\u5bb9\uff1a\n\n\u5927\u5bb6\u597d\uff0c\u4f5c\u4e3a\u4e00\u540d\u66fe\u7ecf\u8d70\u8fc7\u7559\u5b66\u7533\u8bf7\u4e4b\u8def\u7684\u8fc7\u6765\u4eba\uff0c\u6211\u60f3\u548c\u5927\u5bb6\u5206\u4eab\u4e00\u4e9b\u5b9e\u7528\u7684\u7559\u5b66\u7533\u8bf7\u5efa\u8bae\uff0c\u5e2e\u52a9\u5927\u5bb6\u5728\u8fd9\u6761\u9053\u8def\u4e0a\u5c11\u8d70\u5f2f\u8def\uff0c\u987a\u5229\u5b9e\u73b0\u7559\u5b66\u68a6\u60f3\u3002\n\n1. \u63d0\u524d\u89c4\u5212\uff0c\u660e\u786e\u76ee\u6807\n\n\u5728\u7533\u8bf7\u7559\u5b66\u4e4b\u524d\uff0c\u9996\u5148\u8981\u660e\u786e\u81ea\u5df1\u7684\u7559\u5b66\u76ee\u6807\uff0c\u5305\u62ec\u4e13\u4e1a\u65b9\u5411\u3001\u56fd\u5bb6\u5730\u533a\u3001\u5b66\u6821\u6392\u540d\u7b49\u3002\u6b64\u5916\uff0c\u8981\u63d0\u524d\u89c4\u5212\u7533\u8bf7\u65f6\u95f4\u8868\uff0c\u786e\u4fdd\u5404\u9879\u51c6\u5907\u5de5\u4f5c\u6309\u65f6\u5b8c\u6210\u3002\n\n2. \u4e86\u89e3\u7533\u8bf7\u8981\u6c42\uff0c\u505a\u597d\u8d44\u6599\u51c6\u5907\n\n\u6bcf\u4e2a\u56fd\u5bb6\u548c\u5b66\u6821\u7684\u7533\u8bf7\u8981\u6c42\u90fd\u6709\u6240\u4e0d\u540c\uff0c\u4e00\u5b9a\u8981\u63d0\u524d\u4e86\u89e3\u6e05\u695a\u3002\u4e3b\u8981\u5305\u62ec\uff1a\n\n- \u5b66\u672f\u8981\u6c42\uff1a\u6210\u7ee9\u5355\u3001\u5b66\u4f4d\u8bc1\u4e66\u3001\u8bed\u8a00\u6210\u7ee9\u7b49\uff1b\n- \u8d22\u52a1\u8bc1\u660e\uff1a\u5b58\u6b3e\u8bc1\u660e\u3001\u5956\u5b66\u91d1\u7b49\uff1b\n- \u5176\u4ed6\u6750\u6599\uff1a\u4e2a\u4eba\u9648\u8ff0\u3001\u63a8\u8350\u4fe1\u3001\u5b9e\u4e60\u7ecf\u5386\u7b49\u3002\n\n3. \u7a81\u51fa\u4e2a\u4eba\u4f18\u52bf\uff0c\u63d0\u9ad8\u7ade\u4e89\u529b\n\n\u5728\u7533\u8bf7\u6750\u6599\u4e2d\uff0c\u8981\u5145\u5206\u5c55\u793a\u81ea\u5df1\u7684\u4e2a\u4eba\u4f18\u52bf\uff0c\u5982\u5b66\u672f\u6210\u7ee9\u3001\u5b9e\u8df5\u7ecf\u9a8c\u3001\u793e\u56e2\u6d3b\u52a8\u3001\u5fd7\u613f\u8005\u7ecf\u5386\u7b49\u3002\u540c\u65f6\uff0c\u8981\u5b66\u4f1a\u5305\u88c5\u81ea\u5df1\uff0c\u8ba9\u62db\u751f\u5b98\u773c\u524d\u4e00\u4eae\u3002\n\n4. \u6ce8\u91cd\u8bed\u8a00\u80fd\u529b\uff0c\u63d0\u524d\u51c6\u5907\n\n\u8bed\u8a00\u6210\u7ee9\u662f\u7559\u5b66\u7533\u8bf7\u7684\u91cd\u8981\u95e8\u69db\uff0c\u4e00\u5b9a\u8981\u63d0\u524d\u51c6\u5907\u3002\u53ef\u4ee5\u901a\u8fc7\u53c2\u52a0\u8bed\u8a00\u57f9\u8bad\u8bfe\u7a0b\u3001\u6a21\u62df\u8003\u8bd5\u7b49\u65b9\u5f0f\uff0c\u63d0\u9ad8\u81ea\u5df1\u7684\u8bed\u8a00\u6c34\u5e73\u3002\n\n5. \u5bfb\u6c42\u4e13\u4e1a\u6307\u5bfc\uff0c\u63d0\u9ad8\u7533\u8bf7\u6210\u529f\u7387\n\n\u5982\u679c\u5bf9\u7559\u5b66\u7533\u8bf7\u6d41\u7a0b\u4e0d\u719f\u6089\uff0c\u53ef\u4ee5\u5bfb\u6c42\u4e13\u4e1a\u673a\u6784\u7684\u5e2e\u52a9\u3002\u4ed6\u4eec\u53ef\u4ee5\u4e3a\u4f60\u63d0\u4f9b\u9009\u6821\u3001\u6587\u4e66\u8f85\u5bfc\u3001\u9762\u8bd5\u57f9\u8bad\u7b49\u5168\u65b9\u4f4d\u7684\u670d\u52a1\uff0c\u63d0\u9ad8\u7533\u8bf7\u6210\u529f\u7387\u3002\n\n6. \u4fdd\u6301\u79ef\u6781\u5fc3\u6001\uff0c\u9762\u5bf9\u6311\u6218\n\n\u7559\u5b66\u7533\u8bf7\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u5404\u79cd\u56f0\u96be\u548c\u6311\u6218\u3002\u8981\u4fdd\u6301\u79ef\u6781\u5fc3\u6001\uff0c\u52c7\u6562\u9762\u5bf9\uff0c\u540c\u65f6\u4e5f\u8981\u5b66\u4f1a\u8c03\u6574\u9884\u671f\uff0c\u63a5\u53d7\u53ef\u80fd\u7684\u5931\u8d25\u3002\n\n7. \u62d3\u5c55\u4eba\u9645\u5173\u7cfb\uff0c\u4e3a\u7559\u5b66\u52a0\u5206\n\n\u5728\u7533\u8bf7\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u4ee5\u79ef\u6781\u53c2\u52a0\u5404\u79cd\u793e\u4ea4\u6d3b\u52a8\uff0c\u62d3\u5c55\u4eba\u9645\u5173\u7cfb\u3002\u6709\u65f6\u5019\uff0c\u4e00\u4e2a\u5408\u9002\u7684\u63a8\u8350\u4eba\u6216\u8005\u4e00\u6bb5\u4e30\u5bcc\u7684\u5b9e\u8df5\u7ecf\u9a8c\uff0c\u5c31\u80fd\u4e3a\u4f60\u7684\u7559\u5b66\u7533\u8bf7\u52a0\u5206\u3002\n\n\u603b\u4e4b\uff0c\u7559\u5b66\u7533\u8bf7\u662f\u4e00\u4e2a\u6f2b\u957f\u4e14\u5145\u6ee1\u6311\u6218\u7684\u8fc7\u7a0b\uff0c\u5e0c\u671b\u5927\u5bb6\u80fd\u63d0\u524d\u505a\u597d\u51c6\u5907\uff0c\u987a\u5229\u5b9e\u73b0\u7559\u5b66\u68a6\u60f3\u3002\u795d\u613f\u5927\u5bb6\u524d\u7a0b\u4f3c\u9526\uff0c\u5b66\u6709\u6240\u6210\uff01\n\n\u5982\u679c\u4f60\u89c9\u5f97\u8fd9\u7bc7\u653b\u7565\u5bf9\u4f60\u6709\u5e2e\u52a9\uff0c\u8bf7\u70b9\u8d5e\u3001\u6536\u85cf\u5e76\u8f6c\u53d1\u7ed9\u9700\u8981\u7684\u670b\u53cb\u5427\uff01\u8c22\u8c22\uff01",
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"info": "\u5199\u4f5c \u8425\u9500\u6587\u6848\u751f\u6210",
"kimichat": "\u6807\u9898\uff1a\u7559\u5b66\u751f\u7533\u8bf7\u653b\u7565\uff1a\u52a9\u4f60\u5b9e\u73b0\u7559\u5b66\u68a6\u60f3\u7684\u5b9e\u7528\u5efa\u8bae\n\n\u5185\u5bb9\uff1a\n\n\u4eb2\u7231\u7684\u8bfb\u8005\u4eec\uff0c\u7559\u5b66\u662f\u8bb8\u591a\u5b66\u5b50\u7684\u68a6\u60f3\uff0c\u4f46\u7533\u8bf7\u8fc7\u7a0b\u53ef\u80fd\u4f1a\u8ba9\u4f60\u611f\u5230\u56f0\u60d1\u548c\u65e0\u4ece\u4e0b\u624b\u3002\u672c\u6587\u5c06\u4e3a\u4f60\u63d0\u4f9b\u4e00\u4e9b\u5efa\u8bae\uff0c\u5e2e\u52a9\u4f60\u987a\u5229\u7533\u8bf7\u5230\u7406\u60f3\u7684\u7559\u5b66\u9662\u6821\uff0c\u5b9e\u73b0\u4f60\u7684\u7559\u5b66\u68a6\u60f3\u3002\n\n\u4e00\u3001\u660e\u786e\u76ee\u6807\u548c\u89c4\u5212\n\u5728\u7533\u8bf7\u7559\u5b66\u4e4b\u524d\uff0c\u4f60\u9700\u8981\u660e\u786e\u81ea\u5df1\u7684\u76ee\u6807\uff0c\u5305\u62ec\u4e13\u4e1a\u65b9\u5411\u3001\u7559\u5b66\u56fd\u5bb6\u3001\u5b66\u6821\u7c7b\u578b\u7b49\u3002\u8fd9\u5c06\u6709\u52a9\u4e8e\u4f60\u5728\u7533\u8bf7\u8fc7\u7a0b\u4e2d\u505a\u51fa\u660e\u667a\u7684\u9009\u62e9\u3002\u540c\u65f6\uff0c\u5236\u5b9a\u4e00\u4e2a\u957f\u671f\u89c4\u5212\uff0c\u5305\u62ec\u8bed\u8a00\u5b66\u4e60\u3001\u5b9e\u4e60\u7ecf\u5386\u3001\u8bfe\u5916\u6d3b\u52a8\u7b49\uff0c\u4ee5\u63d0\u9ad8\u81ea\u5df1\u7684\u7ade\u4e89\u529b\u3002\n\n\u4e8c\u3001\u63d0\u524d\u51c6\u5907\u8bed\u8a00\u8003\u8bd5\n\u5927\u591a\u6570\u7559\u5b66\u56fd\u5bb6\u90fd\u8981\u6c42\u7533\u8bf7\u8005\u5177\u5907\u4e00\u5b9a\u7684\u8bed\u8a00\u6c34\u5e73\u3002\u56e0\u6b64\uff0c\u63d0\u524d\u51c6\u5907\u6258\u798f\u3001\u96c5\u601d\u7b49\u8bed\u8a00\u8003\u8bd5\u81f3\u5173\u91cd\u8981\u3002\u5236\u5b9a\u4e00\u4e2a\u5408\u7406\u7684\u590d\u4e60\u8ba1\u5212\uff0c\u53c2\u52a0\u57f9\u8bad\u8bfe\u7a0b\u6216\u81ea\u5b66\uff0c\u4e89\u53d6\u5728\u8003\u8bd5\u4e2d\u53d6\u5f97\u7406\u60f3\u6210\u7ee9\u3002\n\n\u4e09\u3001\u4e86\u89e3\u7533\u8bf7\u6d41\u7a0b\u548c\u6750\u6599\n\u6bcf\u4e2a\u56fd\u5bb6\u7684\u7559\u5b66\u7533\u8bf7\u6d41\u7a0b\u548c\u6240\u9700\u6750\u6599\u90fd\u6709\u6240\u4e0d\u540c\u3002\u4f60\u9700\u8981\u63d0\u524d\u4e86\u89e3\u76ee\u6807\u56fd\u5bb6\u548c\u5b66\u6821\u7684\u7533\u8bf7\u8981\u6c42\uff0c\u786e\u4fdd\u5728\u622a\u6b62\u65e5\u671f\u524d\u51c6\u5907\u597d\u6240\u6709\u6750\u6599\u3002\u8fd9\u4e9b\u6750\u6599\u901a\u5e38\u5305\u62ec\u6210\u7ee9\u5355\u3001\u63a8\u8350\u4fe1\u3001\u4e2a\u4eba\u9648\u8ff0\u3001\u7b80\u5386\u7b49\u3002\n\n\u56db\u3001\u9009\u62e9\u5408\u9002\u7684\u63a8\u8350\u4eba\n\u63a8\u8350\u4fe1\u662f\u7559\u5b66\u7533\u8bf7\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\uff0c\u9009\u62e9\u5408\u9002\u7684\u63a8\u8350\u4eba\u5bf9\u4f60\u7684\u7533\u8bf7\u7ed3\u679c\u6709\u5f88\u5927\u5f71\u54cd\u3002\u5efa\u8bae\u9009\u62e9\u4e86\u89e3\u4f60\u5b66\u672f\u80fd\u529b\u548c\u4e2a\u6027\u7279\u70b9\u7684\u8001\u5e08\u6216\u5bfc\u5e08\uff0c\u4ed6\u4eec\u80fd\u591f\u4e3a\u4f60\u63d0\u4f9b\u6709\u529b\u7684\u63a8\u8350\u3002\n\n\u4e94\u3001\u64b0\u5199\u51fa\u8272\u7684\u4e2a\u4eba\u9648\u8ff0\n\u4e2a\u4eba\u9648\u8ff0\u662f\u5c55\u793a\u81ea\u5df1\u7279\u70b9\u548c\u4f18\u52bf\u7684\u91cd\u8981\u9014\u5f84\u3002\u5728\u64b0\u5199\u4e2a\u4eba\u9648\u8ff0\u65f6\uff0c\u8981\u7a81\u51fa\u81ea\u5df1\u7684\u5b66\u672f\u6210\u5c31\u3001\u5b9e\u4e60\u7ecf\u5386\u3001\u8bfe\u5916\u6d3b\u52a8\u7b49\uff0c\u540c\u65f6\u5c55\u793a\u4f60\u5bf9\u672a\u6765\u804c\u4e1a\u89c4\u5212\u7684\u6e05\u6670\u8ba4\u8bc6\u3002\u6ce8\u610f\u907f\u514d\u8fc7\u4e8e\u6cdb\u6cdb\u800c\u8c08\uff0c\u5c3d\u91cf\u7528\u5177\u4f53\u7684\u4f8b\u5b50\u6765\u8bc1\u660e\u81ea\u5df1\u7684\u80fd\u529b\u3002\n\n\u516d\u3001\u7533\u8bf7\u5956\u5b66\u91d1\u548c\u8d44\u52a9\n\u7559\u5b66\u8d39\u7528\u53ef\u80fd\u5f88\u9ad8\uff0c\u56e0\u6b64\u7533\u8bf7\u5956\u5b66\u91d1\u548c\u8d44\u52a9\u662f\u51cf\u8f7b\u7ecf\u6d4e\u8d1f\u62c5\u7684\u6709\u6548\u9014\u5f84\u3002\u4e86\u89e3\u5404\u79cd\u5956\u5b66\u91d1\u7684\u7533\u8bf7\u6761\u4ef6\u548c\u6d41\u7a0b\uff0c\u63d0\u524d\u51c6\u5907\u76f8\u5173\u6750\u6599\uff0c\u63d0\u9ad8\u81ea\u5df1\u7684\u83b7\u5956\u673a\u4f1a\u3002\n\n\u4e03\u3001\u4fdd\u6301\u79ef\u6781\u7684\u5fc3\u6001\n\u7533\u8bf7\u7559\u5b66\u662f\u4e00\u4e2a\u6f2b\u957f\u800c\u5145\u6ee1\u6311\u6218\u7684\u8fc7\u7a0b\uff0c\u4fdd\u6301\u79ef\u6781\u7684\u5fc3\u6001\u81f3\u5173\u91cd\u8981\u3002\u9047\u5230\u632b\u6298\u65f6\uff0c\u4e0d\u8981\u6c14\u9981\uff0c\u8981\u76f8\u4fe1\u81ea\u5df1\u7684\u5b9e\u529b\u548c\u52aa\u529b\u3002\u540c\u65f6\uff0c\u591a\u4e0e\u5bb6\u4eba\u548c\u670b\u53cb\u6c9f\u901a\uff0c\u5bfb\u6c42\u4ed6\u4eec\u7684\u652f\u6301\u548c\u9f13\u52b1\u3002\n\n\u6700\u540e\uff0c\u795d\u4f60\u5728\u7559\u5b66\u7533\u8bf7\u7684\u9053\u8def\u4e0a\u4e00\u5207\u987a\u5229\uff0c\u65e9\u65e5\u5b9e\u73b0\u4f60\u7684\u7559\u5b66\u68a6\u60f3\uff01",
"l2": "\u6807\u9898\uff1a\"\u7559\u5b66\u751f\u7533\u8bf7\u7559\u5b66\uff1a\u5168\u65b9\u4f4d\u6307\u5357\uff0c\u52a9\u4f60\u5f00\u542f\u65b0\u7684\u4eba\u751f\u7bc7\u7ae0\uff01\"\n\n\u5185\u5bb9\uff1a\n\u5927\u5bb6\u597d\uff0c\u4eca\u5929\u6211\u4e3a\u5927\u5bb6\u5206\u4eab\u4e00\u4e9b\u5173\u4e8e\u7559\u5b66\u751f\u7533\u8bf7\u7559\u5b66\u7684\u5efa\u8bae\u3002\u4f5c\u4e3a\u4e00\u540d\u7559\u5b66\u8fc7\u6765\u4eba\uff0c\u6211\u6df1\u77e5\u7533\u8bf7\u8fc7\u7a0b\u4e2d\u7684\u79cd\u79cd\u6311\u6218\u548c\u56f0\u60d1\u3002\u4f46\u662f\uff0c\u4e0d\u8981\u62c5\u5fc3\uff0c\u4ee5\u4e0b\u662f\u6211\u4e3a\u5927\u5bb6\u603b\u7ed3\u7684\u4e00\u4e9b\u7ecf\u9a8c\uff0c\u5e0c\u671b\u80fd\u5e2e\u52a9\u5230\u4f60\u4eec\u3002\n\n1. \u786e\u5b9a\u7559\u5b66\u76ee\u7684\u548c\u65b9\u5411\n\u9996\u5148\uff0c\u8981\u660e\u786e\u81ea\u5df1\u7684\u7559\u5b66\u76ee\u7684\u548c\u65b9\u5411\u3002\u662f\u60f3\u8981\u6df1\u9020\u5b66\u672f\u8fd8\u662f\u62d3\u5bbd\u89c6\u91ce\uff1f\u662f\u60f3\u5728\u67d0\u4e2a\u9886\u57df\u6df1\u8015\u8fd8\u662f\u60f3\u8981\u8de8\u5b66\u79d1\u5b66\u4e60\uff1f\u53ea\u6709\u660e\u786e\u4e86\u81ea\u5df1\u7684\u65b9\u5411\uff0c\u624d\u80fd\u66f4\u597d\u5730\u9009\u62e9\u9002\u5408\u7684\u5b66\u6821\u548c\u4e13\u4e1a\u3002\n\n2. \u63d0\u524d\u4e86\u89e3\u7559\u5b66\u7533\u8bf7\u6d41\u7a0b\n\u7559\u5b66\u7533\u8bf7\u6d41\u7a0b\u590d\u6742\uff0c\u5305\u62ec\u9009\u6821\u3001\u51c6\u5907\u7533\u8bf7\u6750\u6599\u3001\u9762\u8bd5\u3001\u7b7e\u8bc1\u3001\u79df\u623f\u7b49\u3002\u56e0\u6b64\uff0c\u8981\u63d0\u524d\u4e86\u89e3\u6574\u4e2a\u6d41\u7a0b\uff0c\u505a\u597d\u5145\u5206\u7684\u51c6\u5907\u3002\u5efa\u8bae\u53ef\u4ee5\u901a\u8fc7\u5b66\u6821\u5b98\u7f51\u3001\u7559\u5b66\u8bba\u575b\u3001\u54a8\u8be2\u673a\u6784\u7b49\u65b9\u5f0f\u83b7\u53d6\u76f8\u5173\u4fe1\u606f\u3002\n\n3. \u51c6\u5907\u7533\u8bf7\u6750\u6599\n\u7533\u8bf7\u6750\u6599\u662f\u7559\u5b66\u7533\u8bf7\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u73af\u3002\u901a\u5e38\u9700\u8981\u51c6\u5907\u4e2a\u4eba\u7b80\u5386\u3001\u63a8\u8350\u4fe1\u3001\u6210\u7ee9\u5355\u3001\u8bed\u8a00\u6210\u7ee9\u7b49\u3002\u5efa\u8bae\u63d0\u524d\u51c6\u5907\u597d\u6240\u6709\u6750\u6599\uff0c\u5e76\u4ed4\u7ec6\u68c0\u67e5\u662f\u5426\u6709\u9057\u6f0f\u6216\u9519\u8bef\u3002\n\n4. \u53c2\u52a0\u9762\u8bd5\n\u9762\u8bd5\u662f\u7533\u8bf7\u8fc7\u7a0b\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u73af\u3002\u5efa\u8bae\u63d0\u524d\u4e86\u89e3\u9762\u8bd5\u7684\u6d41\u7a0b\u548c\u5e38\u89c1\u95ee\u9898\uff0c\u5e76\u505a\u597d\u5145\u5206\u7684\u51c6\u5907\u3002\u540c\u65f6\uff0c\u8981\u4fdd\u6301\u81ea\u4fe1\u548c\u8c26\u865a\uff0c\u5c55\u793a\u51fa\u81ea\u5df1\u7684\u80fd\u529b\u548c\u7279\u70b9\u3002\n\n5. \u7533\u8bf7\u7b7e\u8bc1\n\u7b7e\u8bc1\u662f\u7559\u5b66\u7533\u8bf7\u7684\u6700\u540e\u4e00\u6b65\u3002\u5efa\u8bae\u63d0\u524d\u4e86\u89e3\u7b7e\u8bc1\u8981\u6c42\u548c\u6d41\u7a0b\uff0c\u5e76\u51c6\u5907\u597d\u6240\u6709\u6750\u6599\u3002\u540c\u65f6\uff0c\u8981\u9075\u5b88\u7b7e\u8bc1\u89c4\u5b9a\uff0c\u907f\u514d\u51fa\u73b0\u8fdd\u89c4\u884c\u4e3a\u3002\n\n6. \u79df\u623f\u548c\u751f\u6d3b\n\u79df\u623f\u548c\u751f\u6d3b\u4e5f\u662f\u7559\u5b66\u751f\u6d3b\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u90e8\u5206\u3002\u5efa\u8bae\u63d0\u524d\u4e86\u89e3\u5f53\u5730\u7684\u79df\u623f\u5e02\u573a\u548c\u60c5\u51b5\uff0c\u505a\u597d\u5145\u5206\u7684\u51c6\u5907\u3002\u540c\u65f6\uff0c\u8981\u9075\u5b88\u5f53\u5730\u7684\u6cd5\u5f8b\u548c\u89c4\u5b9a\uff0c\u6ce8\u610f\u5b89\u5168\u3002\n\n\u4ee5\u4e0a\u5c31\u662f\u6211\u4e3a\u5927\u5bb6\u5206\u4eab\u7684\u4e00\u4e9b\u5173\u4e8e\u7559\u5b66\u751f\u7533\u8bf7\u7559\u5b66\u7684\u5efa\u8bae\u3002\u5e0c\u671b\u8fd9\u4e9b\u7ecf\u9a8c\u80fd\u591f\u5e2e\u52a9\u5230\u4f60\u4eec\uff0c\u795d\u4f60\u4eec\u7559\u5b66\u751f\u6d3b\u987a\u5229\uff01\u5982\u679c\u4f60\u4eec\u6709\u4efb\u4f55\u5176\u4ed6\u95ee\u9898\uff0c\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u7559\u8a00\u6216\u8005\u79c1\u4fe1\u6211\u3002",
"question": "\u5e2e\u6211\u5199\u4e00\u7bc7\u7559\u5b66\u751f\u7533\u8bf7\u7559\u5b66\u5efa\u8bae\u7684\u5c0f\u7ea2\u4e66\u6807\u9898\u548c\u5185\u5bb9"
},
"metadata": {},
"responses": [
{
"status": "submitted",
"user_id": "45d72340-1083-4837-b74e-696cf9ec762d",
"values": {
"accurate-answer": {
"value": "\u662f"
},
"bed-case": {
"value": "\u5426"
},
"completeness": {
"value": "\u597d"
},
"follow-instructions": {
"value": "\u662f"
},
"preference": {
"value": [
{
"rank": 4,
"value": "L2"
},
{
"rank": 3,
"value": "GPT4"
},
{
"rank": 1,
"value": "GLM4"
},
{
"rank": 2,
"value": "KiMiChat"
}
]
},
"richness": {
"value": "\u4e0d\u597d"
},
"specification": {
"value": "\u4e0d\u597d"
},
"suggestion": {
"value": "\u5185\u5bb9\u53ef\u4ee5\uff0c\u53ef\u4ee5\u518d\u4e30\u5bcc\u4e00\u70b9\uff0c\u683c\u5f0f\u6700\u597d\u5206\u5927\u5c0f\u6807\u9898"
},
"synthesis": {
"value": "\u4e0d\u597d"
}
}
}
],
"suggestions": [],
"vectors": {}
}
```

While the same record in HuggingFace `datasets` looks as follows:

```json
{
"accurate-answer": [
{
"status": "submitted",
"user_id": "45d72340-1083-4837-b74e-696cf9ec762d",
"value": "\u662f"
}
],
"accurate-answer-suggestion": null,
"accurate-answer-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"bed-case": [
{
"status": "submitted",
"user_id": "45d72340-1083-4837-b74e-696cf9ec762d",
"value": "\u5426"
}
],
"bed-case-suggestion": null,
"bed-case-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"completeness": [
{
"status": "submitted",
"user_id": "45d72340-1083-4837-b74e-696cf9ec762d",
"value": "\u597d"
}
],
"completeness-suggestion": null,
"completeness-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"external_id": null,
"follow-instructions": [
{
"status": "submitted",
"user_id": "45d72340-1083-4837-b74e-696cf9ec762d",
"value": "\u662f"
}
],
"follow-instructions-suggestion": null,
"follow-instructions-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
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"info": "\u5199\u4f5c \u8425\u9500\u6587\u6848\u751f\u6210",
"kimichat": "\u6807\u9898\uff1a\u7559\u5b66\u751f\u7533\u8bf7\u653b\u7565\uff1a\u52a9\u4f60\u5b9e\u73b0\u7559\u5b66\u68a6\u60f3\u7684\u5b9e\u7528\u5efa\u8bae\n\n\u5185\u5bb9\uff1a\n\n\u4eb2\u7231\u7684\u8bfb\u8005\u4eec\uff0c\u7559\u5b66\u662f\u8bb8\u591a\u5b66\u5b50\u7684\u68a6\u60f3\uff0c\u4f46\u7533\u8bf7\u8fc7\u7a0b\u53ef\u80fd\u4f1a\u8ba9\u4f60\u611f\u5230\u56f0\u60d1\u548c\u65e0\u4ece\u4e0b\u624b\u3002\u672c\u6587\u5c06\u4e3a\u4f60\u63d0\u4f9b\u4e00\u4e9b\u5efa\u8bae\uff0c\u5e2e\u52a9\u4f60\u987a\u5229\u7533\u8bf7\u5230\u7406\u60f3\u7684\u7559\u5b66\u9662\u6821\uff0c\u5b9e\u73b0\u4f60\u7684\u7559\u5b66\u68a6\u60f3\u3002\n\n\u4e00\u3001\u660e\u786e\u76ee\u6807\u548c\u89c4\u5212\n\u5728\u7533\u8bf7\u7559\u5b66\u4e4b\u524d\uff0c\u4f60\u9700\u8981\u660e\u786e\u81ea\u5df1\u7684\u76ee\u6807\uff0c\u5305\u62ec\u4e13\u4e1a\u65b9\u5411\u3001\u7559\u5b66\u56fd\u5bb6\u3001\u5b66\u6821\u7c7b\u578b\u7b49\u3002\u8fd9\u5c06\u6709\u52a9\u4e8e\u4f60\u5728\u7533\u8bf7\u8fc7\u7a0b\u4e2d\u505a\u51fa\u660e\u667a\u7684\u9009\u62e9\u3002\u540c\u65f6\uff0c\u5236\u5b9a\u4e00\u4e2a\u957f\u671f\u89c4\u5212\uff0c\u5305\u62ec\u8bed\u8a00\u5b66\u4e60\u3001\u5b9e\u4e60\u7ecf\u5386\u3001\u8bfe\u5916\u6d3b\u52a8\u7b49\uff0c\u4ee5\u63d0\u9ad8\u81ea\u5df1\u7684\u7ade\u4e89\u529b\u3002\n\n\u4e8c\u3001\u63d0\u524d\u51c6\u5907\u8bed\u8a00\u8003\u8bd5\n\u5927\u591a\u6570\u7559\u5b66\u56fd\u5bb6\u90fd\u8981\u6c42\u7533\u8bf7\u8005\u5177\u5907\u4e00\u5b9a\u7684\u8bed\u8a00\u6c34\u5e73\u3002\u56e0\u6b64\uff0c\u63d0\u524d\u51c6\u5907\u6258\u798f\u3001\u96c5\u601d\u7b49\u8bed\u8a00\u8003\u8bd5\u81f3\u5173\u91cd\u8981\u3002\u5236\u5b9a\u4e00\u4e2a\u5408\u7406\u7684\u590d\u4e60\u8ba1\u5212\uff0c\u53c2\u52a0\u57f9\u8bad\u8bfe\u7a0b\u6216\u81ea\u5b66\uff0c\u4e89\u53d6\u5728\u8003\u8bd5\u4e2d\u53d6\u5f97\u7406\u60f3\u6210\u7ee9\u3002\n\n\u4e09\u3001\u4e86\u89e3\u7533\u8bf7\u6d41\u7a0b\u548c\u6750\u6599\n\u6bcf\u4e2a\u56fd\u5bb6\u7684\u7559\u5b66\u7533\u8bf7\u6d41\u7a0b\u548c\u6240\u9700\u6750\u6599\u90fd\u6709\u6240\u4e0d\u540c\u3002\u4f60\u9700\u8981\u63d0\u524d\u4e86\u89e3\u76ee\u6807\u56fd\u5bb6\u548c\u5b66\u6821\u7684\u7533\u8bf7\u8981\u6c42\uff0c\u786e\u4fdd\u5728\u622a\u6b62\u65e5\u671f\u524d\u51c6\u5907\u597d\u6240\u6709\u6750\u6599\u3002\u8fd9\u4e9b\u6750\u6599\u901a\u5e38\u5305\u62ec\u6210\u7ee9\u5355\u3001\u63a8\u8350\u4fe1\u3001\u4e2a\u4eba\u9648\u8ff0\u3001\u7b80\u5386\u7b49\u3002\n\n\u56db\u3001\u9009\u62e9\u5408\u9002\u7684\u63a8\u8350\u4eba\n\u63a8\u8350\u4fe1\u662f\u7559\u5b66\u7533\u8bf7\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\uff0c\u9009\u62e9\u5408\u9002\u7684\u63a8\u8350\u4eba\u5bf9\u4f60\u7684\u7533\u8bf7\u7ed3\u679c\u6709\u5f88\u5927\u5f71\u54cd\u3002\u5efa\u8bae\u9009\u62e9\u4e86\u89e3\u4f60\u5b66\u672f\u80fd\u529b\u548c\u4e2a\u6027\u7279\u70b9\u7684\u8001\u5e08\u6216\u5bfc\u5e08\uff0c\u4ed6\u4eec\u80fd\u591f\u4e3a\u4f60\u63d0\u4f9b\u6709\u529b\u7684\u63a8\u8350\u3002\n\n\u4e94\u3001\u64b0\u5199\u51fa\u8272\u7684\u4e2a\u4eba\u9648\u8ff0\n\u4e2a\u4eba\u9648\u8ff0\u662f\u5c55\u793a\u81ea\u5df1\u7279\u70b9\u548c\u4f18\u52bf\u7684\u91cd\u8981\u9014\u5f84\u3002\u5728\u64b0\u5199\u4e2a\u4eba\u9648\u8ff0\u65f6\uff0c\u8981\u7a81\u51fa\u81ea\u5df1\u7684\u5b66\u672f\u6210\u5c31\u3001\u5b9e\u4e60\u7ecf\u5386\u3001\u8bfe\u5916\u6d3b\u52a8\u7b49\uff0c\u540c\u65f6\u5c55\u793a\u4f60\u5bf9\u672a\u6765\u804c\u4e1a\u89c4\u5212\u7684\u6e05\u6670\u8ba4\u8bc6\u3002\u6ce8\u610f\u907f\u514d\u8fc7\u4e8e\u6cdb\u6cdb\u800c\u8c08\uff0c\u5c3d\u91cf\u7528\u5177\u4f53\u7684\u4f8b\u5b50\u6765\u8bc1\u660e\u81ea\u5df1\u7684\u80fd\u529b\u3002\n\n\u516d\u3001\u7533\u8bf7\u5956\u5b66\u91d1\u548c\u8d44\u52a9\n\u7559\u5b66\u8d39\u7528\u53ef\u80fd\u5f88\u9ad8\uff0c\u56e0\u6b64\u7533\u8bf7\u5956\u5b66\u91d1\u548c\u8d44\u52a9\u662f\u51cf\u8f7b\u7ecf\u6d4e\u8d1f\u62c5\u7684\u6709\u6548\u9014\u5f84\u3002\u4e86\u89e3\u5404\u79cd\u5956\u5b66\u91d1\u7684\u7533\u8bf7\u6761\u4ef6\u548c\u6d41\u7a0b\uff0c\u63d0\u524d\u51c6\u5907\u76f8\u5173\u6750\u6599\uff0c\u63d0\u9ad8\u81ea\u5df1\u7684\u83b7\u5956\u673a\u4f1a\u3002\n\n\u4e03\u3001\u4fdd\u6301\u79ef\u6781\u7684\u5fc3\u6001\n\u7533\u8bf7\u7559\u5b66\u662f\u4e00\u4e2a\u6f2b\u957f\u800c\u5145\u6ee1\u6311\u6218\u7684\u8fc7\u7a0b\uff0c\u4fdd\u6301\u79ef\u6781\u7684\u5fc3\u6001\u81f3\u5173\u91cd\u8981\u3002\u9047\u5230\u632b\u6298\u65f6\uff0c\u4e0d\u8981\u6c14\u9981\uff0c\u8981\u76f8\u4fe1\u81ea\u5df1\u7684\u5b9e\u529b\u548c\u52aa\u529b\u3002\u540c\u65f6\uff0c\u591a\u4e0e\u5bb6\u4eba\u548c\u670b\u53cb\u6c9f\u901a\uff0c\u5bfb\u6c42\u4ed6\u4eec\u7684\u652f\u6301\u548c\u9f13\u52b1\u3002\n\n\u6700\u540e\uff0c\u795d\u4f60\u5728\u7559\u5b66\u7533\u8bf7\u7684\u9053\u8def\u4e0a\u4e00\u5207\u987a\u5229\uff0c\u65e9\u65e5\u5b9e\u73b0\u4f60\u7684\u7559\u5b66\u68a6\u60f3\uff01",
"l2": "\u6807\u9898\uff1a\"\u7559\u5b66\u751f\u7533\u8bf7\u7559\u5b66\uff1a\u5168\u65b9\u4f4d\u6307\u5357\uff0c\u52a9\u4f60\u5f00\u542f\u65b0\u7684\u4eba\u751f\u7bc7\u7ae0\uff01\"\n\n\u5185\u5bb9\uff1a\n\u5927\u5bb6\u597d\uff0c\u4eca\u5929\u6211\u4e3a\u5927\u5bb6\u5206\u4eab\u4e00\u4e9b\u5173\u4e8e\u7559\u5b66\u751f\u7533\u8bf7\u7559\u5b66\u7684\u5efa\u8bae\u3002\u4f5c\u4e3a\u4e00\u540d\u7559\u5b66\u8fc7\u6765\u4eba\uff0c\u6211\u6df1\u77e5\u7533\u8bf7\u8fc7\u7a0b\u4e2d\u7684\u79cd\u79cd\u6311\u6218\u548c\u56f0\u60d1\u3002\u4f46\u662f\uff0c\u4e0d\u8981\u62c5\u5fc3\uff0c\u4ee5\u4e0b\u662f\u6211\u4e3a\u5927\u5bb6\u603b\u7ed3\u7684\u4e00\u4e9b\u7ecf\u9a8c\uff0c\u5e0c\u671b\u80fd\u5e2e\u52a9\u5230\u4f60\u4eec\u3002\n\n1. \u786e\u5b9a\u7559\u5b66\u76ee\u7684\u548c\u65b9\u5411\n\u9996\u5148\uff0c\u8981\u660e\u786e\u81ea\u5df1\u7684\u7559\u5b66\u76ee\u7684\u548c\u65b9\u5411\u3002\u662f\u60f3\u8981\u6df1\u9020\u5b66\u672f\u8fd8\u662f\u62d3\u5bbd\u89c6\u91ce\uff1f\u662f\u60f3\u5728\u67d0\u4e2a\u9886\u57df\u6df1\u8015\u8fd8\u662f\u60f3\u8981\u8de8\u5b66\u79d1\u5b66\u4e60\uff1f\u53ea\u6709\u660e\u786e\u4e86\u81ea\u5df1\u7684\u65b9\u5411\uff0c\u624d\u80fd\u66f4\u597d\u5730\u9009\u62e9\u9002\u5408\u7684\u5b66\u6821\u548c\u4e13\u4e1a\u3002\n\n2. \u63d0\u524d\u4e86\u89e3\u7559\u5b66\u7533\u8bf7\u6d41\u7a0b\n\u7559\u5b66\u7533\u8bf7\u6d41\u7a0b\u590d\u6742\uff0c\u5305\u62ec\u9009\u6821\u3001\u51c6\u5907\u7533\u8bf7\u6750\u6599\u3001\u9762\u8bd5\u3001\u7b7e\u8bc1\u3001\u79df\u623f\u7b49\u3002\u56e0\u6b64\uff0c\u8981\u63d0\u524d\u4e86\u89e3\u6574\u4e2a\u6d41\u7a0b\uff0c\u505a\u597d\u5145\u5206\u7684\u51c6\u5907\u3002\u5efa\u8bae\u53ef\u4ee5\u901a\u8fc7\u5b66\u6821\u5b98\u7f51\u3001\u7559\u5b66\u8bba\u575b\u3001\u54a8\u8be2\u673a\u6784\u7b49\u65b9\u5f0f\u83b7\u53d6\u76f8\u5173\u4fe1\u606f\u3002\n\n3. \u51c6\u5907\u7533\u8bf7\u6750\u6599\n\u7533\u8bf7\u6750\u6599\u662f\u7559\u5b66\u7533\u8bf7\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u73af\u3002\u901a\u5e38\u9700\u8981\u51c6\u5907\u4e2a\u4eba\u7b80\u5386\u3001\u63a8\u8350\u4fe1\u3001\u6210\u7ee9\u5355\u3001\u8bed\u8a00\u6210\u7ee9\u7b49\u3002\u5efa\u8bae\u63d0\u524d\u51c6\u5907\u597d\u6240\u6709\u6750\u6599\uff0c\u5e76\u4ed4\u7ec6\u68c0\u67e5\u662f\u5426\u6709\u9057\u6f0f\u6216\u9519\u8bef\u3002\n\n4. \u53c2\u52a0\u9762\u8bd5\n\u9762\u8bd5\u662f\u7533\u8bf7\u8fc7\u7a0b\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u73af\u3002\u5efa\u8bae\u63d0\u524d\u4e86\u89e3\u9762\u8bd5\u7684\u6d41\u7a0b\u548c\u5e38\u89c1\u95ee\u9898\uff0c\u5e76\u505a\u597d\u5145\u5206\u7684\u51c6\u5907\u3002\u540c\u65f6\uff0c\u8981\u4fdd\u6301\u81ea\u4fe1\u548c\u8c26\u865a\uff0c\u5c55\u793a\u51fa\u81ea\u5df1\u7684\u80fd\u529b\u548c\u7279\u70b9\u3002\n\n5. \u7533\u8bf7\u7b7e\u8bc1\n\u7b7e\u8bc1\u662f\u7559\u5b66\u7533\u8bf7\u7684\u6700\u540e\u4e00\u6b65\u3002\u5efa\u8bae\u63d0\u524d\u4e86\u89e3\u7b7e\u8bc1\u8981\u6c42\u548c\u6d41\u7a0b\uff0c\u5e76\u51c6\u5907\u597d\u6240\u6709\u6750\u6599\u3002\u540c\u65f6\uff0c\u8981\u9075\u5b88\u7b7e\u8bc1\u89c4\u5b9a\uff0c\u907f\u514d\u51fa\u73b0\u8fdd\u89c4\u884c\u4e3a\u3002\n\n6. \u79df\u623f\u548c\u751f\u6d3b\n\u79df\u623f\u548c\u751f\u6d3b\u4e5f\u662f\u7559\u5b66\u751f\u6d3b\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u90e8\u5206\u3002\u5efa\u8bae\u63d0\u524d\u4e86\u89e3\u5f53\u5730\u7684\u79df\u623f\u5e02\u573a\u548c\u60c5\u51b5\uff0c\u505a\u597d\u5145\u5206\u7684\u51c6\u5907\u3002\u540c\u65f6\uff0c\u8981\u9075\u5b88\u5f53\u5730\u7684\u6cd5\u5f8b\u548c\u89c4\u5b9a\uff0c\u6ce8\u610f\u5b89\u5168\u3002\n\n\u4ee5\u4e0a\u5c31\u662f\u6211\u4e3a\u5927\u5bb6\u5206\u4eab\u7684\u4e00\u4e9b\u5173\u4e8e\u7559\u5b66\u751f\u7533\u8bf7\u7559\u5b66\u7684\u5efa\u8bae\u3002\u5e0c\u671b\u8fd9\u4e9b\u7ecf\u9a8c\u80fd\u591f\u5e2e\u52a9\u5230\u4f60\u4eec\uff0c\u795d\u4f60\u4eec\u7559\u5b66\u751f\u6d3b\u987a\u5229\uff01\u5982\u679c\u4f60\u4eec\u6709\u4efb\u4f55\u5176\u4ed6\u95ee\u9898\uff0c\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u7559\u8a00\u6216\u8005\u79c1\u4fe1\u6211\u3002",
"metadata": "{}",
"preference": [
{
"status": "submitted",
"user_id": "45d72340-1083-4837-b74e-696cf9ec762d",
"value": {
"rank": [
4,
3,
1,
2
],
"value": [
"L2",
"GPT4",
"GLM4",
"KiMiChat"
]
}
}
],
"preference-suggestion": null,
"preference-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"question": "\u5e2e\u6211\u5199\u4e00\u7bc7\u7559\u5b66\u751f\u7533\u8bf7\u7559\u5b66\u5efa\u8bae\u7684\u5c0f\u7ea2\u4e66\u6807\u9898\u548c\u5185\u5bb9",
"richness": [
{
"status": "submitted",
"user_id": "45d72340-1083-4837-b74e-696cf9ec762d",
"value": "\u4e0d\u597d"
}
],
"richness-suggestion": null,
"richness-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"specification": [
{
"status": "submitted",
"user_id": "45d72340-1083-4837-b74e-696cf9ec762d",
"value": "\u4e0d\u597d"
}
],
"specification-suggestion": null,
"specification-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"suggestion": [
{
"status": "submitted",
"user_id": "45d72340-1083-4837-b74e-696cf9ec762d",
"value": "\u5185\u5bb9\u53ef\u4ee5\uff0c\u53ef\u4ee5\u518d\u4e30\u5bcc\u4e00\u70b9\uff0c\u683c\u5f0f\u6700\u597d\u5206\u5927\u5c0f\u6807\u9898"
}
],
"suggestion-suggestion": null,
"suggestion-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"synthesis": [
{
"status": "submitted",
"user_id": "45d72340-1083-4837-b74e-696cf9ec762d",
"value": "\u4e0d\u597d"
}
],
"synthesis-suggestion": null,
"synthesis-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
}
}
```

### Data Fields

Among the dataset fields, we differentiate between the following:

* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.

* **info** is of type `text`.
* **question** is of type `text`.
* **l2** is of type `text`.
* **gpt4** is of type `text`.
* **glm4** is of type `text`.
* **kimichat** is of type `text`.

* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.

* **bed-case** is of type `label_selection` with the following allowed values ['是', '否'].
* (optional) **follow-instructions** is of type `label_selection` with the following allowed values ['是', '否'].
* (optional) **accurate-answer** is of type `label_selection` with the following allowed values ['是', '否'].
* (optional) **richness** is of type `label_selection` with the following allowed values ['好', '不好'].
* (optional) **completeness** is of type `label_selection` with the following allowed values ['好', '不好'].
* (optional) **specification** is of type `label_selection` with the following allowed values ['好', '不好'].
* (optional) **synthesis** is of type `label_selection` with the following allowed values ['好', '不好'].
* (optional) **preference** is of type `ranking` with the following allowed values ['L2', 'GPT4', 'GLM4', 'KiMiChat'].
* (optional) **suggestion** is of type `text`.

* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.

* (optional) **bed-case-suggestion** is of type `label_selection` with the following allowed values ['是', '否'].
* (optional) **follow-instructions-suggestion** is of type `label_selection` with the following allowed values ['是', '否'].
* (optional) **accurate-answer-suggestion** is of type `label_selection` with the following allowed values ['是', '否'].
* (optional) **richness-suggestion** is of type `label_selection` with the following allowed values ['好', '不好'].
* (optional) **completeness-suggestion** is of type `label_selection` with the following allowed values ['好', '不好'].
* (optional) **specification-suggestion** is of type `label_selection` with the following allowed values ['好', '不好'].
* (optional) **synthesis-suggestion** is of type `label_selection` with the following allowed values ['好', '不好'].
* (optional) **preference-suggestion** is of type `ranking` with the following allowed values ['L2', 'GPT4', 'GLM4', 'KiMiChat'].
* (optional) **suggestion-suggestion** is of type `text`.

Additionally, we also have two more fields that are optional and are the following:

* **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.

### Data Splits

The dataset contains a single split, which is `train`.

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation guidelines

[More Information Needed]

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

[More Information Needed]

原始信息汇总

数据集概述

数据集名称

• 名称: first-dataset

数据集创建工具

• 创建工具: Argilla

数据集大小

• 大小: 小于1000个记录

数据集标签

• 标签:
  • rlfh
  • argilla
  • human-feedback

数据集配置

• 配置文件: 包含一个名为argilla.yaml的配置文件，用于配置数据集。

数据集记录格式

• 记录格式: 与HuggingFace datasets兼容，可通过FeedbackDataset.from_huggingface或datasets库的load_dataset方法加载。

数据集结构

• 数据结构: 包含字段、问题、建议、元数据、向量和指南。
  • 字段: 目前仅支持文本字段。
  • 问题: 包括多种类型，如评分、文本、标签选择、多标签选择或排名。
  • 建议: 与问题关联，提供辅助注释者的建议。
  • 元数据: 提供关于数据集记录的额外信息，如作者、日期或来源。
  • 指南: 提供给注释者的指导信息。

数据集加载方式

• 通过Argilla加载: 使用pip install argilla --upgrade安装Argilla后，通过以下代码加载数据集： python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("CodeTheory/first-dataset")

• 通过datasets加载: 使用pip install datasets --upgrade安装datasets后，通过以下代码加载数据集： python from datasets import load_dataset ds = load_dataset("CodeTheory/first-dataset")

数据集用途

• 用途: 可用于多种NLP任务，具体取决于配置。

数据集语言

• 语言: 信息不足，需要更多信息。

数据集实例

• 实例: 数据集实例示例，展示数据集的结构和内容。

数据集详细结构

字段详情

• 字段名称: info, question, l2, gpt4, glm4, kimichat
• 类型: 文本
• 是否必需: 是
• 支持Markdown: 是

问题详情

• 问题名称: bed-case, follow-instructions, accurate-answer, richness, completeness, specification, synthesis, preference, suggestion
• 类型: 标签选择、排名、文本
• 是否必需: 部分必需
• 值/标签: 如[是, 否]，[好, 不好]等

建议详情

• 建议: 与问题关联，提供辅助注释者的建议。

元数据详情

• 元数据名称: 未提供具体名称
• 类型: 未提供
• 值: 未提供
• 对注释者可见: 未提供

指南详情

• 指南: 提供给注释者的指导信息，具体内容未提供。