You’re not actually disagreeing with me, you’re just restating that the process is fallible. No argument there. All reasoning models are fallible, including humans. The difference is, LLMs are consistently fallible, in ways that can be measured, improved, and debugged (unlike humans, who are wildly inconsistent, emotionally reactive, and prone to motivated reasoning).

Also, the fact that LLMs are “trained on tools like logic and discourse” isn’t a weakness. That’s how any system, including humans, learns to reason. We don’t emerge from the womb with innate logic, we absorb it from language, culture, and experience. You’re applying a double standard: fallibility invalidates the LLM, but not the human brain? Come on.

And your appeal to “fuck around and find out” isn’t a disqualifier; it’s an opportunity. LLMs already assist in experiment design, hypothesis testing, and even simulating edge cases. They don’t run the scientific method independently (yet), but they absolutely enhance it.

So again: no one’s saying LLMs are perfect. The claim is they’re useful in evaluating truth claims, often more so than unaided human intuition. The fact that you’ve encountered hallucinations doesn’t negate that - it just proves the tool has limits, like every tool. The difference is, this one keeps getting better.

Edit: I’m not describing a “reasoning model” layered on top of an LLM. I’m describing what a large language model is and does at its core. Reasoning emerges from the statistical training on language patterns. It’s not a separate tool it uses, and it’s not “trained on logic and discourse” as external modules. Logic and discourse are simply part of the training data; meaning they’re embedded into the weights through gradient descent, not bolted on as tools.

No, I’m specifically describing what an LLM is. It’s a statistical model trained on token sequences to generate contextually appropriate outputs. That’s not “tools it uses", that is the model. When I said it pattern-matches reasoning and identifies contradictions, I wasn’t talking about external plug-ins or retrieval tools, I meant the LLM’s own internal learned representation of language, logic, and discourse.

You’re drawing a false distinction. When GPT flags contradictions, weighs claims, or mirrors structured reasoning, it’s not outsourcing that to some other tool, it’s doing what it was trained to do. It doesn’t need to understand truth like a human to model the structure of truthful argumentation, especially if the prompt constrains it toward epistemic rigor.

Now, if you’re talking about things like code execution, search, or retrieval-augmented generation, then sure, those are tools it can use. But none of that was part of my argument. The ability to track coherence, cite counterexamples, or spot logical fallacies is all within the base LLM. That’s just weights and training.

So unless your point is that LLMs aren’t humans, which is obvious and irrelevant, all you’ve done is attack your own straw man.

I do understand what an LLM is. It’s a probabilistic model trained on massive corpora to predict the most likely next token given a context window. I know it’s not sentient and doesn’t “think,” and doesn’t have beliefs. That’s not in dispute.

But none of that disqualifies it from being useful in evaluating truth claims. Evaluating truth isn’t about thinking in the human sense, it’s about pattern-matching valid reasoning, sourcing relevant evidence, and identifying contradictions or unsupported claims. LLMs do that very well, especially when prompted properly.

Your insistence that this is “dangerous naïveté” confuses two very different things: trusting an LLM blindly, versus leveraging it with informed oversight. I’m not saying GPT magically knows truth, I’m saying it can be used as a tool in a truth-seeking process, just like search engines, logic textbooks, or scientific journals. None of those are conscious either, yet we use them to get closer to truth.

You’re worried about misuse, and so am I. But claiming the tool is inherently useless because it lacks consciousness is like saying microscopes can’t discover bacteria because they don’t know what they’re looking at.

So again: if you believe GPT is inherently incapable of aiding in truth evaluation, the burden’s on you to propose a more effective tool that’s publicly accessible, scalable, and consistent. I’ll wait.

Right now, the capabilities of LLM’s are the worst they’ll ever be. It could literally be tomorrow that someone drops and LLM that would be perfectly calibrated to evaluate truth claims. But right now, we’re at least 90% of the way there.

The reason people fail to understand the untruths of AI is the same reason people hurt themselves with power tools, or use a calculator wrong.

You don’t blame the tool, you blame the user. LLM’s are no different. You can prompt GPT to intentionally give you bad info, or lead it to give you bad info by posting increasingly deranged statements. If you stay coherent, well read and make an attempt at structuring arguments to the best of your ability, the pool of data GPT pulls from narrows enough to be more useful than anything else I know.

I’m curious as to what you regard as a better tool for evaluating truth?

Period.

What makes you think humans are better at evaluating truth? Most people can’t even define what they mean by “truth,” let alone apply epistemic rigor. Tweak it a little, and Gpt is more consistent and applies reasoning patterns that outperform the average human by miles.

Epistemology isn’t some mystical art, it’s a structured method for assessing belief and justification, and large models approximate it surprisingly well. Sure it doesn’t “understand” truth in the human sense, but it does evaluate claims against internalized patterns of logic, evidence, and coherence based on a massive corpus of human discourse. That’s more than most people manage in a Facebook argument.

So yes, it can evaluate truth. Not perfectly, but often better than the average person.

I’m good enough at noticing my own flaws, as not to be arrogant enough to believe I’m immune from making mistakes :p

Granted, it is flakey unless you’ve configured it not to be a shit cunt. Before I manually set these prompts and memory references, it talked shit all the time.

Actually, given the aforementioned prompts, its quite good at discerning flaws in my arguments and logical contradictions.

I’ve also trained its memory not to make assumptions when it comes to contentious topics, and to always source reputable articles and link them to replies.

I often use it to check whether my rationale is correct, or if my opinions are valid.

This is the reason I’ve deliberately customized GPT with the follow prompts:

• User expects correction if words or phrases are used incorrectly.

• Tell it straight—no sugar-coating.

• Stay skeptical and question things.

• Keep a forward-thinking mindset.

• User values deep, rational argumentation.

• Ensure reasoning is solid and well-supported.

• User expects brutal honesty.

• Challenge weak or harmful ideas directly, no holds barred.

• User prefers directness.

• Point out flaws and errors immediately, without hesitation.

• User appreciates when assumptions are challenged.

• If something lacks support, dig deeper and challenge it.

I suggest copying these prompts into your own settings if you use GPT or other glorified chatbots.

Australia’s politics are weird. Our ‘liberal’ party is the conservative one. Our ‘nationals’ party is meant to represent the people in rural communities, but are somehow more corrupt and full of shit than the ‘liberals’. Our ‘labor’ party has literally never given birth, and nobody in ‘The Greens’ is actually green. Needless to say, Australia is weird.

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