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I would imagine the number of people who choose Claude code or Codex because it gives a political opinion they like rather than producing quality code is pretty close to zero.


Training to ignore evidence and logic in one domain transfers to reasoning degradation in other domains.


You assume your highly charged political query is hitting the main LLM at all and not some external short circuit.


You're assuming that your prompt is not being intercepted and rerouted by a lightweight prompt classification model.

In addition, you can make a similar comparison between Chinese models refusing to answer questions about Tiananmen Square and OpenAI and Anthropic models refusing to answer questions about the synthesis of methamphetamine; I don't think these topic by topic refusals would have real impacts on the overall performances of frontier LLMs.


Is this actually documented?

Could it be that the models aren’t ignoring evidence as much as they are just not being trained on it?


I choose not to use Grok because I don't want to hear about a made up white genocide in South Africa...


Would be interesting to see something like that pop up during a coding session.


What you see here, is the iterator, i! For i, less than - a million black people killed by racist genocide, call the function save_lives(), i++




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