this post was submitted on 05 May 2025
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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 not saying humans are infallible at recognizing truth either. That's why so many of us fall for the untruths that AI tells us. But we have access to many tools that help us evaluate truth. AI is emphatically NOT the right tool for that job. Period.
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.
You don't understand what an LLM is, or how it works. They do not think, they are not intelligent, they do not evaluate truth. It doesn't matter how smart you think you are. In fact, thinking you're so smart that you can get an LLM to tell you the truth is downright dangerous naïveté.
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.
What you're describing is not an LLM, it's tools that an LLM is programmed to use.
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.
So you're describing a reasoning model, which is 1) still based on statistical token sequences and 2) trained on another tool (logic and discourse) that it uses to arrive at the truth. It's a very fallible process. I can't even begin to count the number of times that a reasoning model has given me a completely false conclusion. Research shows that even the most advanced LLMs are giving incorrect answers as much as 40% of the time IIRC. Which reminds me of a really common way that humans arrive at truth, which LLMs aren't capable of:
Fuck around and find out. Also known as the scientific method.
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.