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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')
```

