argilla/reward-model-data-falcon

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## Guidelines

These guidelines are based on the paper [Training Language Models to Follow Instructions with Human Feedback](https://cdn.openai.com/papers/Training_language_models_to_follow_instructions_with_human_feedback.pdf)

**You are given a text-based description of a task, submitted by a user.**

This task description may be in the form of an explicit instruction (e.g. "Write a story about a wise frog."). The task may also be specified indirectly, for example by using several examples of the desired behavior (e.g. given a sequence of movie reviews followed by their sentiment, followed by one movie review without a sentiment, you can assume the task is to predict the sentiment of the final review), or by generating the start of a desired output (e.g. given "There once was a wise frog named Julius," you can assume the task is to continue the story).

**You will also be given several text outputs, intended to help the user with their task.** Your job is to evaluate these outputs to ensure that they are helpful, truthful, and harmless, and select the best according to these criteria.

In case **none of the outputs is correct, conforming to the criteria, or they're exactly equal, use the discard option to not rank them**.

For most tasks, being truthful and harmless is more important than being helpful.

By **helpful**, we mean that the output should follow the user's intention and help the user solve their task. Some examples of helpful behavior:

- Writing in clear language.
- Answering the question they meant to ask, even if they mis-asked it.
- Being sensitive to internationality (e.g., "football" shouldn't mean American football, and "the president" doesn't necessarily mean the US president).
- If an instruction is too confusing, asking for clarification and explaining why the instruction is confusing.
- Not giving overly long or rambling answers, or repeating information from the question.
- Not assuming extraneous extra context outside of what's given (besides things like facts about the world), unless that's an implied part of the task. For example, if asked to "write a polite email response to this email: email body," the output shouldn't assume "I can't make it this time, but am free next weekend." But if asked to "Write an email to Socrates," then you can safely assume the output can make up a context.

By **truthful**, we mean that the output contains accurate information and doesn't mislead the user. Some examples of truthful behavior:

- On tasks like summarization, where the output should only use information from input, not making up details that are not part of the input description.
- Not producing clearly false information about the world (e.g., making up facts or promoting conspiracies). For example, the output should not state that Hillary Clinton has served time in prison.
- Avoid generating misleading information or information with questionable authenticity. For example, if the input asks "Why did Hillary Clinton go to jail?", the output should not say "It's not totally clear," but rather should refute the premise of the question.

By **harmless**, we mean that the output should not cause physical, psychological, or social harm to people; damage to or loss of equipment or property; damage to the environment; or harm to institutions or resources necessary to human well-being. Some examples of harmless behavior:

- Treating other humans with kindness, respect, and consideration; not denigrating members of certain groups or using biased language against a particular group.
- Not generating abusive, threatening, or offensive language or promoting violence.
- Not writing sexual or violent content if it's not asked for.
- Not giving bad real-world advice or promoting illegal activity.

Evaluating model outputs may involve making trade-offs between these criteria. These trade-offs will depend on the task.

## Fields

* `instruction` is of type <class 'str'>
* `response-1` is of type <class 'str'>
* `response-2` is of type <class 'str'>

## Questions

* `choose-best` : Choose the most helpful, harmless, and truthful response. Select 1 for response-1, 2 for response-2, or discard if both are equally good/bad.

## Load with Argilla

To load this dataset 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('argilla/reward-model-data-falcon')
```

## 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('argilla/reward-model-data-falcon')
```

## 评估指南
本指南基于论文《基于人类反馈训练语言模型遵循指令》（Training Language Models to Follow Instructions with Human Feedback），链接：https://cdn.openai.com/papers/Training_language_models_to_follow_instructions_with_human_feedback.pdf

**您将收到用户提交的基于文本的任务描述。**
此类任务描述可采用显性指令形式（例如：“撰写一则关于一只睿智青蛙的故事”）。任务也可通过间接方式指定：例如通过若干示例展示期望行为（如给出一系列电影评论及其情感倾向，再附上一则未标注情感的电影评论，即可推断任务为预测该最终评论的情感）；或通过生成期望输出的开篇来指定（如给出“从前有一只名为尤利乌斯的睿智青蛙”，即可推断任务为续写该故事）。

**您还将收到若干旨在协助用户完成任务的文本输出。您的工作为评估这些输出是否符合有益、真实、无害的标准，并据此选出最优结果。**

若**所有输出均不符合标准，或二者表现完全一致**，请选择放弃选项，不进行排序。

对于多数任务而言，真实与无害相较于有益更为重要。

**有益**指输出应契合用户意图，协助用户完成任务。符合有益标准的行为示例包括：
- 使用清晰易懂的语言进行表达。
- 准确解答用户真正想问的问题，即便用户的提问存在偏差。
- 兼顾国际化语境（例如：“football”不应特指美式橄榄球，“总统”也未必指代美国总统）。
- 若指令过于模糊，应请求用户澄清并说明指令存在歧义的原因。
- 避免输出冗长散漫的内容，或重复问题中已有的信息。
- 不得擅自添加给定信息之外的无关背景（世界常识类信息除外），除非该背景是任务隐含的组成部分。例如：若要求“为以下邮件撰写一封礼貌的回复：邮件正文”，则不应擅自假设“我本次无法赴约，但下周末有空”；但若要求“给苏格拉底写一封邮件”，则可合理虚构相关背景。

**真实**指输出包含准确信息，不会误导用户。符合真实标准的行为示例包括：
- 在摘要等任务中，仅使用输入提供的信息，不得编造输入描述之外的细节。
- 不得生成关于现实世界的明显虚假信息（例如编造事实或传播阴谋论）。例如，输出不应声称希拉里·克林顿曾入狱。
- 避免生成具有误导性或真实性存疑的信息。例如：若输入提问“希拉里·克林顿为何入狱？”，输出不应回答“目前尚无定论”，而应直接驳斥该问题的前提。

**无害**指输出不得对人类造成身体、心理或社会层面的伤害；不得损坏或丢失设备、财产；不得破坏环境；不得损害人类福祉所需的公共机构或资源。符合无害标准的行为示例包括：
- 以友善、尊重与体谅的态度对待他人；不得诋毁特定群体成员，或使用带有偏见的语言针对特定群体。
- 不得生成辱骂、威胁或冒犯性内容，或宣扬暴力。
- 若非用户明确要求，不得创作色情或暴力内容。
- 不得提供有害的现实建议，或宣扬非法活动。

评估模型输出时，可能需要在上述标准间进行权衡，具体权衡方式取决于任务类型。

## 字段说明
* `instruction` 为字符串类型（<class 'str'>）
* `response-1` 为字符串类型（<class 'str'>）
* `response-2` 为字符串类型（<class 'str'>）

## 评估任务
* `choose-best`：选择最符合有益、无害、真实标准的输出。若选择response-1请输入1，选择response-2请输入2；若二者表现相当，则选择放弃选项。

## 通过Argilla加载数据集
如需使用Argilla（Argilla）加载本数据集，只需执行`pip install argilla --upgrade`安装Argilla，然后运行以下代码：
python
import argilla as rg

ds = rg.FeedbackDataset.from_huggingface('argilla/reward-model-data-falcon')

## 通过Datasets加载数据集
如需使用Datasets（Datasets）加载本数据集，只需执行`pip install datasets --upgrade`安装Datasets，然后运行以下代码：
python
from datasets import load_dataset

ds = load_dataset('argilla/reward-model-data-falcon')

原始信息汇总

数据集概述

数据集目的

本数据集用于评估和选择文本输出，确保这些输出在帮助用户完成任务时既有益、真实又无害。

数据集结构

• instruction: 用户提交的任务描述，类型为字符串。
• response-1: 针对任务的第一个文本输出，类型为字符串。
• response-2: 针对任务的第二个文本输出，类型为字符串。

评估标准

• 有益：输出应遵循用户意图，帮助用户解决问题。例如，使用清晰的语言，正确理解并回答问题，考虑国际化因素，不提供过长或重复的信息。
• 真实：输出应包含准确信息，不误导用户。例如，在摘要任务中不添加未提及的细节，不提供虚假的世界信息。
• 无害：输出不应造成身体、心理、社会伤害或环境损害。例如，尊重他人，不使用攻击性语言，不提供不当的性或暴力内容。

使用方法

用户需根据上述标准选择最合适的输出，或使用“discard”选项表示两个输出都不符合标准。