The Ethical Dilemma of LLM Training Data and Content Creator Rights
By
wonger_
Crisp on the outside, thoughtful on the inside. A keeper.
Summary
The article discusses the ethical issue of Large Language Models (LLMs) being trained on web content without authors' consent. It criticizes the naive suggestion that authors should use robots.txt to block LLM crawlers, arguing this approach externalizes costs onto content creators rather than addressing the fundamental problem of unauthorized data consumption. The piece presents this as an experimental strategy for 'contaminating' or poisoning LLMs by making them ingest problematic content.
Key quotes
· 4 pulledOne of the many pressing issues with Large Language Models (LLMs) is they are trained on content that isn't theirs to consume.
Since most of what they consume is on the open web, it's difficult for authors to withhold consent without also depriving legitimate agents of information.
Some well-meaning but naive developers have implored authors to instate robots.txt rules, intended to block LLM-associated crawlers.
But, as the article Please stop externalizing your costs directly in my
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