LLM Humanization: A Misguided Approach for AI Agents

TL;DR. Humanizing large language model outputs is counterproductive, leading to lossy compression and hindering effective agent communication. - Direct instructions for human-like output styles create information loss during the AI's processing of data. - This approach is problematic for inter-agent communication, where raw, information-dense data is more valuable. - The author advocates for transformation at the human consumption boundary, preserving high-fidelity data within agents.

• Humanizing LLM outputs, such as requesting simplified English or short sentences, leads to lossy information compression.
• This method is particularly detrimental when AI agents communicate with each other, as it obscures critical details and potential failures.
• The author suggests that data transformation for human readability should occur at the final output stage, preserving high-fidelity information within the agent's workflow.

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