Quantifying 'Humanness' in LLM Outputs: A Founder's Challenge

The 'Ask HN' post highlights a common challenge in developing AI-powered writing tools: making LLM outputs sound natural and human-like. The founder of Aaptics, a tool designed to help founders draft content, is struggling to prevent the underlying models from sounding like a 'corporate robot.' They are currently using a custom RAG setup with negative prompting and few-shot examples, but the model still occasionally slips into a recognizable 'ChatGPT tone.' The founder is seeking advice on how to quantitatively evaluate the 'humanness' of LLM outputs, exploring options such as LLM-as-a-judge frameworks, temperature/top_p tweaking, and hardcoding penalizations for certain n-grams. This challenge underscores the importance of fine-tuning and refining LLMs to produce content that is both informative and engaging. The success of AI writing tools hinges on their ability to generate text that resonates with human readers and avoids sounding artificial or robotic.

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