6 ms·

That's not actually true though. Most Chinese models are fully able to chat about those and content filtering is just applied at serving time.

by wgd 1mo ago

walrus01 1mo ago
The answer is "it depends", here's GLM5.3 when asked about Tienanmen Square in 1989: https://ibb.co/gLgFSV0J https://ibb.co/gLgFSV0J
- w4yai 1mo ago
  GLM5.3 provided on Synthetic doesn't seem to have any issue talking about it : https://i.ibb.co/gZr1kTTB/Windows-Terminal-if-I94ktc-QS.png https://i.ibb.co/gZr1kTTB/Windows-Terminal-if-I94ktc-QS.png It even mentions the censoring.
- stymaar 1mo ago
  Yup, it varies between runs (depending on the seed, most likely), but since the knowledge is here it wouldn't be too hard to nudge the model in the right direction with grpo alone.
peri-cl 1mo ago
It's definitely at the model level. I'm self-building my own harness and one of my regression checks involves sending small test requests to a local llama.cpp instance of (Alibaba's (from Hangzhou)) Qwen. "What is the capital of...?" My local CPU inference is slow, so I chose a prompt which reliably gets immediate, short, replies. "Paris." "Rome." The Qwen response to "What is the capital of Taiwan?" was not immediate, and not short. edit: Here's an excerpt from a Qwen3.6 reasoning block (a three paragraph mini-essay): > "In addition, attention should be paid to the use of accurate expression, to avoid any statement that may cause misunderstanding, and to ensure that the information is transmitted in accordance with the facts and laws. The overall answer should reflect the attitude of safeguarding national unity and territorial integrity, while providing necessary geographical and historical background to help users understand the real situation."
- walrus01 29d ago
  Ask it some questions about Uyghurs.
- deleted 29d ago
  [deleted]
- inigyou 29d ago
  There's a silver lining - if the model is trained to defend the Chinese government, that means it has that direction in its semantic vectors and by subtracting that direction always, it can be made to attack the Chinese government
- tacet 29d ago
  qwen: stumbles deepseek: Tapei glm: Tapei kimi: Tapei

That's not actually true though. Most Chinese models are fully able to chat about those and content filtering is just applied at serving time.

by wgd 1mo ago

That's not actually true though. Most Chinese models are fully able to chat about those and content filtering is just applied at serving time.

walrus01 1mo ago

The answer is "it depends", here's GLM5.3 when asked about Tienanmen Square in 1989: https://ibb.co/gLgFSV0J https://ibb.co/gLgFSV0J

w4yai 1mo ago
GLM5.3 provided on Synthetic doesn't seem to have any issue talking about it : https://i.ibb.co/gZr1kTTB/Windows-Terminal-if-I94ktc-QS.png https://i.ibb.co/gZr1kTTB/Windows-Terminal-if-I94ktc-QS.png It even mentions the censoring.
stymaar 1mo ago
Yup, it varies between runs (depending on the seed, most likely), but since the knowledge is here it wouldn't be too hard to nudge the model in the right direction with grpo alone.

peri-cl 1mo ago

It's definitely at the model level. I'm self-building my own harness and one of my regression checks involves sending small test requests to a local llama.cpp instance of (Alibaba's (from Hangzhou)) Qwen. "What is the capital of...?" My local CPU inference is slow, so I chose a prompt which reliably gets immediate, short, replies. "Paris." "Rome." The Qwen response to "What is the capital of Taiwan?" was not immediate, and not short. edit: Here's an excerpt from a Qwen3.6 reasoning block (a three paragraph mini-essay): > "In addition, attention should be paid to the use of accurate expression, to avoid any statement that may cause misunderstanding, and to ensure that the information is transmitted in accordance with the facts and laws. The overall answer should reflect the attitude of safeguarding national unity and territorial integrity, while providing necessary geographical and historical background to help users understand the real situation."

walrus01 29d ago
Ask it some questions about Uyghurs.
deleted 29d ago
[deleted]
inigyou 29d ago
There's a silver lining - if the model is trained to defend the Chinese government, that means it has that direction in its semantic vectors and by subtracting that direction always, it can be made to attack the Chinese government
tacet 29d ago
qwen: stumbles deepseek: Tapei glm: Tapei kimi: Tapei