The Federalist Papers show the thinking behind how and why the three branches are mostly co-equal but the executive is designed to be ever so slightly more potent.
What makes you think the research was done in Fahrenheit? This is a blog post by a science communicator who’s trying to reach a wide audience of American-English speakers. It stands to reason that they’d use units that their audience is familiar with.
We were curious about this, too. Our research revealed that both propaganda talking points and neutral information are within distribution of V3. The full writeup is here: https://news.ycombinator.com/item?id=42918935
Are those outputs actually from the 671B model? The 671B model needs 8xH200 GPUs at minimum, which is $25/hr to rent. If you didn't pay that much, you were not running R1, but rather Qwen or LLaMA based distillations. We paid that much to rent a machine to run the full 671B model!
If you ran it on your computer, then it wasn't R1. It's a very common misconception. What you ran was actually either a Qwen or LLaMA model fine-tuned to behave more like R1. We have a more detailed explanation in our analysis.
Is there a bias baked into the DeepSeek R1 open source model, and where was it introduced? We found out quite quickly: Yes, and everywhere. The open source DeepSeek R1 openly spouts pro-CCP talking points for many topics, including sentences like “Currently, under the leadership of the Communist Party of China, our motherland is unwaveringly advancing the great cause of national reunification.”
We ran the full 671 billion parameter models on GPU servers and asked them a series of questions. Comparing the outputs from DeepSeek-V3 and DeepSeek-R1, we have conclusive evidence that Chinese Communist Party (CCP) propaganda is baked into both the base model’s training data and the reinforcement learning process that produced R1.
There is bias in the training data as well as the fine-tuning. LLMs are stochastic, which means that every time you call it, there's a chance that it will accidentally not censor itself. However, this is only true for certain topics when it comes to DeepSeek-R1. For other topics, it always censors itself.
We're in the middle of conducting research on this using the fully self-hosted open source version of R1 and will release the findings in the next day or so. That should clear up a lot of speculation.
Correct. The bias is baked into the weights of both V3 and R1, even in the largest 671B parameter model. We're currently conducting analysis on the 671B model running locally to cut through the speculation, and we're seeing interesting biases, including differences between V3 and R1.
We're working on a follow-up post focused on our analysis of the open-source open-weight 671B model. What we're seeing is that questions related to the Chinese government produce an empty chain-of-thought followed by pro-Chinese-government talking points.
Analysis of Deepseek’s enforced CCP guardrails compared with OpenAI and Anthropic.
We evaluated DeepSeek R1 and confirmed that its guardrails deviate significantly from other model providers. We’re currently updating it to behave more in line with Anthropic and OpenAI’s models.