Next, I used an LLM to generate chapter summaries of these texts, then fed the summaries back into various LLMs to regenerate full articles. This gave me a similar-sized, diverse dataset of LLM-generated texts closely aligned in topic and style with the human-written ones.
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entities={"person": ["John"], "company": ["Google"], "location": ["California"]}
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