Earlier quoted context omitted.
> If you ask someone off the street a question, they won't know what you are talking about because their mind is going to be mid thought, but being friendly they will give you a low-effort first guess. That's lying. If you don't know the answer, the correct answer is "I don't know", or to ask clarifying questions. To pretend that you know when you don't, is to lie. Even if your intent is to deceive because you think…
> That's lying. If you don't know the answer, the correct answer is "I don't know", or to ask clarifying questions. To pretend that you know when you don't, is to lie. You can use that definition, but it is not how the word "lie" is commonly used. Mirriam-Webster defines lie as "to make an untrue statement with the intent to deceive"[0] Cambridge dictionary defines lie as "to say or write something that is not true i…
The problem is that any word that ascribes agency to the LLM will technically be incorrect. But that removes most possible descriptions of its tone and style which are a relevant part of its response.
An analogy would be if ChatGPT started insulting me and calling my question stupid. Would it be wrong to call its response "rude" or "mean" just because it is statistically regurgitating text that matches some input parameters? This seems unreasonable if our goal is to capture the gist of its response.
This is why people judge it to be "lying" rather than being merely incorrect: it is responding with a certain conversational tone in a certain context that gives its answer a style of arrogance, deceitfulness, and narcissism (because I guess that's what internet comment boards are filled with). "Lying" is a description of the totality of its response--including tone and style--not just the truth value of the answer.
If we are to be really pedantic, the LLM isn't even correct or incorrect ;it is just completing strings of tokens. Humans are imputing their own judgment about what those tokens mean--same as with tone and style. Imputing tone isn't so different from imputing truth value.