Value Over Language Model: Detecting Original Contribution in Writing
Quick summary
arXiv:2609.00700v1 Announce Type: new Abstract: LLMs have been rapidly adopted across writing tasks, prompting the development of tools for detecting LLM-generated text. Yet, these tools largely measure how much of a document's surface text was written by an LLM and aren't fundamentally designed to measure how much of the information content or ideas originated from the LLM itself rather than being supplied by the user in the prompt. In this work, we design a framework that measures how much value a person adds on top of what a language model could have easily produced by itself. The method re
Key takeaways
- arXiv:2609.00700v1 Announce Type: new Abstract: LLMs have been rapidly adopted across writing tasks, prompting the development of tools for detecting LLM-generated text.
- Yet, these tools largely measure how much of a document's surface text was written by an LLM and aren't fundamentally designed to measure how much of the information content or ideas originated from the LLM itself rather than being supplied by the user in the prompt.
- In this work, we design a framework that measures how much value a person adds on top of what a language model could have easily produced by itself.
Why it matters
“Value Over Language Model: Detecting Original Contribution in Writing” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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