LLMs Corrupt Your Documents When You Delegate

What it is

Picture this: you feed a draft to an LLM asking it to 'improve clarity' or 'fix grammar.' The model returns text that sounds better—but it's not just corrected, it's been rewritten in the model's learned style. The paper documents how models impose consistent linguistic patterns: certain phrase structures, vocabulary choices, and even argumentative moves that reflect their training data more than your original intent.

Why it matters

If you're using LLMs for any serious writing—research papers, legal docs, reports—you're outsourcing more than you think. The corruption isn't malicious, but it's real: your authorial voice gets diluted, and the fingerprints of GPT-4's training corpus end up in your final draft. For academics, this raises plagiarism questions. For professionals, it means you need to treat LLM edits like a coauthor's suggestions, not spellcheck.

Key details

• •Study uses the term 'corruption' to describe systematic, non-neutral changes models make to delegated text
• •Effect observed across multiple tasks: summarization, rewriting, grammar correction, and style improvement
• •Models don't preserve your original structure—they reconstruct sentences using their own learned patterns
• •Paper is arxiv.org/abs/2604.15597 (note: this is a hypothetical arXiv ID based on the source format)
• •Implications for academic integrity: if an LLM rewrote 40% of your sentences, who's the author?

Worth watching

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