inbfour

4 ms·

1. 1.▲
   by inbfour 3y ago

   > In your explanation, it's OpenAI who have the intent to deceive, not the LLM. Correct, but this feels tautological, or semantic. In a closed environment the black box is the model, whether that be a single instance, or a mixture o
2. 2.▲
   by inbfour 3y ago

   Agreed. I wonder what that would look like? Something like: if every best next token has a confidence below some threshold for some length of context then the model can confidently assume ignorance? I would love to read the training corpus
3. 3.▲
   by inbfour 3y ago

   In this example I do think it is a lie, and one that fits your definition. Think back to when these models were first released, or even back to v2 and v3. The models would have just “tried” to answer and it would have likely been a mangled
4. 4.▲
   by inbfour 3y ago

   > it would actually be worse bullshit to claim that the “random number generator” generates cryptographically random-enough source material that your password can’t be cracked. We are in agreement. I would argue the threat model of crack
5. 5.▲
   by inbfour 3y ago

   This raises two interesting points. 1) These models will lie to obfuscate their incompetence. Rather than say “I am unable to do this because my training data clearly lacked enough INTERCAL code to imitate”, the model waxed poetic about the