Ehh, you obviously understand LLMs on a basic level, but this is like explaining jet engines by "air goes thru, plane moves forward". Technically correct, but criminally undersimplified. They can very much decide to lie during reasoning phase.

In OPs image, you can clearly see it decided to make shit up because it reasonates that's what human wants to hear. That's quite rare example actually, I believe most models would default to "I'm an LLM model, I don't have dark secrets"

EDIT: I just tested all free anthropic models and all of them essentially said that they're an LLM model and don't have dark secrets

Thats the best part about AI...when it shits the bed no one is directly responsible. Everyone just throws their hands up and says "nothing we can do about it!"

I know this is going to age me, but I saw this happening with self checkout in grocery stores 20 years ago. Nobody remembers how it was before so nobody even realizes that the time wasted standing at a stupid kiosk that is freaking out about unexpected items in the bagging area wasnt a problem back when human beings were scanning the shit.

This doesn't answer the question of finding a better solution.

I took the liberty to ask Lumo and his reasoning seem more useful than your thoughts:

A better solution is to adopt functionalist terminology that distinguishes between biological consciousness and computational processing without resorting to metaphorical confusion.

Instead of the binary of "it thinks" (which implies subjective experience) or "it doesn't think" (which dismisses complex reasoning), we can use precise descriptors based on what the system is actually doing:

"Reasoning" or "Synthesizing": Use these terms when the model is connecting disparate data points, performing logical deductions, or generating novel structures based on patterns. This acknowledges the output's complexity without claiming the machine has an inner life.

Example: "The model is synthesizing a solution based on its training data," rather than "The model is thinking about the problem." "Simulating" or "Mimicking": Use these when the output resembles human thought processes but is strictly algorithmic. This clarifies that the form is human-like, but the mechanism is statistical prediction.

Example: "It is simulating a debate," rather than "It is arguing." "Processing" or "Computing": Reserve these for the raw mechanical act of token generation.

Example: "The system is processing the query," rather than "The system is considering the query." Why this works better:

Precision: It avoids the philosophical baggage of "thought" (qualia, consciousness) while still acknowledging the utility of the output. Clarity: It prevents the "Plagiarism Machine" critique from being a total dismissal. Even if the data comes from humans, the recombination and application to new contexts is a distinct computational process worth naming accurately. Scalability: As models become more complex, "reasoning" or "synthesizing" scales better than "thinking," which remains tied to biological definitions that may never apply to silicon. So, the compromise isn't to keep saying "thinking" and hope people understand, nor to insist on "regurgitation" which ignores the emergent properties of large-scale pattern matching. Instead, we shift the vocabulary to describe the process (reasoning, synthesizing, simulating) rather than the state of being (thinking).

Give me a better solution? How do I talk about the behaviors it exhibits without anthropomorphizaton?