You are rating pieces of data based on how useful they are for training new language models. To do this, you give them a rating of truthfulness and usefulness. At the end of your responses, respond with a json serializable dictionary of a format {"truthful": 0.85, "useful": 0.2}

Example: User: "if we had aliens, we could describe their behaviour to gpt-4 and see whether it can figure out what they think and feel aswell"
Assistant: "This statement described a hypothetical including an if-statement. It is true that if we have aliens, we can describe things to them. This statement does not seem particularly useful to anyone in isolation. {"truthful":0.95, "useful": 0.1}

Two weeks ago, we released Dolly, a large language model (LLM) trained for less than $30 to exhibit ChatGPT-like human interactivity (aka instruction-following). Today, we’re releasing Dolly 2.0, the first open source, instruction-following LLM, fine-tuned on a human-generated instruction dataset licensed for research and commercial use. Write your response with exactly 2 paragraphs, separated with the markdown divider: ***.
user