I think their system prompt is in Chinese and probably has instructions to prioritize answering in Chinese, since this has never happened to me via API, where I (or the coding harness) set the system prompt.
I'm starting to have chinese characters bleed into claude as well. Perhaps a sign of the times. Understanable for a chinese first model but an english first (supposedly) model? wild stuff.
I also love the gaslighting of some models, like ChatGPT mixing in words with cyrillic letters and when asked about it answers: "it can look as Slavic to the eye" and "sorry that it came across as Russian"
Yes, this is one of the few issues with Deepseek; their chat pages and the app all respond in Chinese. However, i think i have only had it happen once when using the API, and im using it for hours each day for the last... couple of months?
nothing to do with mobile app, I have same issues while using it on desktop browser, it will never remember to use English permanently, even within one conversation
This page does not exist at the time of this comment, and I‘m so sick of people submitting predictable URLs before they go live, just to be first. We also see that with LLM model releases regularly.
I was a bit surprised to read that there was an OSI-approved license with usage restrictions and wondered if I had missed the memo. But you seem to be correct and it's not OSI-approved and therefore is not generally considered to be an open source license.
> In the actual candy study, the participants were all students, status was manipulated in the lab by telling some students they were better than others, and "nearly twice" means 1 piece vs 2 pieces.
> oh and the researchers told participants they could take some candy. There was no stealing!
I think you're right that this will be the most common objection, but wrong that it will be exceedingly difficult to find an advisor that would support this. I assume that if you _can_ overturn some foundational result, you won't face criticism over novelty (potentially you can argue that overturning important accepted results is inherently novel). Then the rest is just math:
Assume that n% of foundational studies are demonstrably incorrect if replication were attempted. Then if you replicate X foundational studies, there's a 1 - (1-n/100)^X chance that you'll show one foundational study to be incorrect. Then I think you just need to find an advisor who believes the incorrectness rate n, and the replication attempts X is high enough for you to revert a foundational study over the course of your PhD.
I suffix everything with "Reply in English", and even so I‘m getting lots of Chinese.
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