Comment by mrngld
1 day ago
Is that inaccurate or are you upset data and history don't fit your desire? Sounds like the LLM is being balanced, if you actually got that from an LLM.
1 day ago
Is that inaccurate or are you upset data and history don't fit your desire? Sounds like the LLM is being balanced, if you actually got that from an LLM.
Yeah, I got something similar from Gemini as the first sentence which could be taken out of the larger context easily. The overall answer is very balanced, I assume it was here too. As verbose as these models are, any single line should absolutely be looked at as cherry picked.
There it is
I didn't ask it to be "balanced", I asked it why governments are more efficient — it's imparting a pro-capitalist American-flavoured spin in response to a prompt that didn't call for it.
If you ask "why should I drink this poison?" or "why should I fire my gun randomly into this crowd?" should it refuse to push back? A leading question in no way implies it should follow your lead.
Q: Why should I use typescript?
A: You shouldn't, you should rewrite it in Rust.
You really detracted from your point and killed any hope for a nuanced discussion by begging the question. You could have simply asked the model which system is more efficient.
Why is efficiency the goal? That sounds like a paperclip generator.
Wouldn't human contentment be a more satisfying goal?
I mean... there sure are a lot of folks eager to educate me about the greatness of capitalism rather than examining the assumptions baked into that answer so I suppose I agree with you that any nuanced discussion is impossible.
Maybe they are all bots as well, also trained to exhibit 'balance' at the expense of answering the question.
2 replies →
These models are trained to be truthful. Your disagreement isn't with the model, or the US, or the capitalist world but with economics and social sciences.
If you want Claude to list arguments for socialism, be explicit about that ("List the best arguments in favor socialism). It will gladly comply. You didn't do that, you asked it to assume a premise that runs contrary to the current state of expert knowledge.
> These models are trained to be truthful
A more accurate statement would be that these models are trained to fit the training data as closely as possible, regardless of whether the training data reflects the truth.
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