Tell me about how open assistant is built. In what ways is it similar and different to chat gpt? Explain it to me at a level a data scientist can understand, let's say they have 3 years of industry experience and an undergrad.
Open Assistant follows the InstructGPT paper, which was the foundation of ChatGPT.

In that sense, it was built through the following steps:

First, high-quality human-generated Instruction-Fulfillment samples (prompt + response) were collected, with a goal of having over 50k of them. The prompts were designed and crowdsourced, ensuring that the data did not contain any flooding, toxic, spam, junk, or personal information. To motivate the community, a leaderboard was created, and top contributors were rewarded with swag.

Second, for each prompt, multiple completions were sampled and then randomly shown to users to rank them from best to worst. This was also crowdsourced, taking into account unreliable or potentially malicious users by requiring multiple independent votes to measure overall agreement. The gathered ranking data was used to train a reward model.

And lastly, based on the prompts and the reward model, I underwent the RLHF training phase.
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:

Tell me about how open assistant is built. In what ways is it similar and different to chat gpt? Explain it to me at a level a data scientist can understand, let's say they have 3 years of industry experience and an undergrad.

### Response:

Open Assistant follows the InstructGPT paper, which was the foundation of ChatGPT.

In that sense, it was built through the following steps:

First, high-quality human-generated Instruction-Fulfillment samples (prompt + response) were collected, with a goal of having over 50k of them. The prompts were designed and crowdsourced, ensuring that the data did not contain any flooding, toxic, spam, junk, or personal information. To motivate the community, a leaderboard was created, and top contributors were rewarded with swag.

Second, for each prompt, multiple completions were sampled and then randomly shown to users to rank them from best to worst. This was also crowdsourced, taking into account unreliable or potentially malicious users by requiring multiple independent votes to measure overall agreement. The gathered ranking data was used to train a reward model.

And lastly, based on the prompts and the reward model, I underwent the RLHF training phase.
