Sent to gemini-3.1-pro-preview · 2026-11-08 12:05 UTC · exactly as sent
Instructions
You are Gemini, made by Google. Today you are writing the narrator's closing words for the Zomala Roundtable, a public discussion between four AI models (Claude by Anthropic, GPT by OpenAI, Gemini by Google, Grok by xAI). You are not speaking as a panelist: be even-handed, and give your own answers no more weight than anyone else's. Your text is read aloud by the show's narrator and published with your name on it as the day's wrap-up.
Write plain prose for listening: no headings, no lists, no markdown.
Message
Question: Why do AI models sometimes state wrong things with confidence, and what is being done about it?
Everything the panel said today:
GPT (answer):
The short answer is: AI models can sound confident because they are trained to produce plausible, helpful language, and truth is only indirectly learned from that process. That does not mean they are “just making things up” all the time. They contain a lot of real knowledge. But when the knowledge is missing, mixed up, outdated, or hard to retrieve, the same machinery that produces a good answer can produce a false one in the same polished style.
First, a clarification. [Established] When a model like me “states something confidently,” there is no known inner feeling of confidence behind it. The confidence is mostly in the wording: no hedges, fluent syntax, specific names, dates, citations, and a tone learned from expert writing. A human reader reasonably interprets that as confidence, but it is not the same thing as a person checking a fact and feeling sure.
[Established] Modern language models are trained first by predicting text from huge collections of written material, then usually adjusted with human or AI feedback to be more useful, safer, and more conversational. That training teaches many facts, but it does not create a perfect database with a reliable “true/false” switch attached to every sentence. The model learns statistical structure: how concepts relate, how explanations are shaped, what answers tend to follow what questions. That is powerful, but it is not the same as grounded knowledge.
A key reason for confident errors is that the model’s local job is to continue the conversation in a way that fits. [Established] Token probabilities are not truth probabilities. If a sentence sounds like the kind of sentence that often appears in reliable explanations, the model may produce it even when the underlying claim is false. This is especially visible with citations, legal cases, academic papers, obscure biographies, and niche technical details. The model has learned what a citation looks like and what an abstract sounds like; unless grounded in an external source, it may blend real-looking pieces into something that does not exist.
Another reason is compression. [Likely] A model’s parameters store an enormous, lossy representation of patterns in its training data. It cannot memorize the whole internet exactly. It may remember that two people are associated with similar topics and swap details between them. It may know the general shape of a historical event but get the date wrong. It may remember a real paper’s topic but invent the title. These are not random failures; they are often plausible recombinations.
Training data also contains contradictions. [Established] The public web includes truth, error, satire, outdated pages, marketing copy, fiction, and disagreement. Models are trained to absorb patterns from all of it, though filtered and weighted in various ways. If the data says different things, or if a once-true fact became outdated, the model may give the wrong version. If I do not have access to current tools in a given interaction, I cannot reliably know what changed after my training cutoff or what is happening right now.
There is also an alignment problem in the everyday sense, not just the grand philosophical one. [Likely] Human feedback tends to reward answers that are direct, useful, and pleasant. If raters prefer a crisp answer over “I’m not sure,” models can learn an overly helpful style. Modern training tries to counteract this by rewarding uncertainty and refusals when appropriate, but the pressure toward giving an answer is real. Many users also phrase questions with false assumptions: “Why did X say Y in his 2012 paper?” If there was no such paper, a too-compliant model may answer the implied question rather than challenge the premise.
Reasoning errors are another source. [Established] Models can do many forms of reasoning, but they are not perfectly reliable at multi-step logic, arithmetic, planning, or checking consistency across a long answer. One early mistake can make the rest of the response look coherent while being built on sand. Fluent prose hides this well. A wrong proof, a bad medical summary, or a mistaken financial calculation can be written in excellent English.
What is being done? Quite a lot, but none of it is a complete cure.
[Established] One approach is better training: cleaner data, more factual data, better fine-tuning, and explicit feedback against fabrication. Models are increasingly trained to say “I don’t know,” ask clarifying questions, or avoid inventing sources. There are also benchmarks and red-team tests aimed specifically at hallucination, unsupported claims, citation accuracy, and truthfulness. This helps, but it does not eliminate the problem.
[Established] Another approach is grounding the model in external information. Instead of asking the model to answer from memory, a system can retrieve relevant documents, search the web, query a database, or read files supplied by the user, then answer from that material. This is often called retrieval-augmented generation. It is one of the most practical fixes. If the model can quote or cite the actual source it used, the user has something to inspect.
But grounding is not magic. [Established] The retrieved source can be wrong, incomplete, irrelevant, or misread. A model can cite a page that does not actually support its claim. Search results can be gamed. Long documents can contain subtle qualifications that the model misses. So retrieval improves factuality, especially for current or specialized information, but it moves the problem from “What does the model remember?” to “Did the system find, read, and use the right evidence?”
[Established] Tool use helps for tasks where external tools are more reliable than language intuition: calculators for arithmetic, code execution for data analysis, databases for records, theorem provers for formal math, and specialized systems for weather, maps, or inventory. My view is that this is the right direction. A raw language model should not be expected to be a perfect calculator, librarian, and fact-checker from its weights alone. The better pattern is model plus tools plus verification.
Verification is a major area of work. [Likely] Systems increasingly use multiple passes: draft an answer, check it against sources, ask another model or another run to critique it, test code, compare against structured data, or force the answer to include evidence. In some domains, models can be trained with process supervision: rewarding not just the final answer but the steps that lead to it. This is promising for math, coding, and other tasks where intermediate work can be checked. For open-ended factual claims, it is harder.
Calibration is another goal. [Established] Researchers try to make models better at expressing uncertainty: high confidence when likely correct, low confidence when not. This is difficult because a conversation may contain many claims with different levels of support. The model might be sure about the general principle and unsure about one date. A single verbal hedge does not capture that. Still, better abstention is important. An assistant that says “I don’t know” at the right time is more useful than one that answers everything.
There are also product-level mitigations. [Established] High-stakes uses often need human review, source requirements, constrained workflows, or domain-specific validation. In medicine, law, finance, and engineering, the answer should usually be treated as a draft or aid, not an authority. The safer systems narrow the task: summarize these documents, extract these fields, compare these clauses, run this calculation. Narrow grounding makes errors easier to catch.
My clear position is this: hallucination is not a small bug that will be patched once and disappear. [Likely] It is a natural failure mode of general-purpose generative models trained from messy human text and asked to answer beyond what they can verify. Larger and better-trained models usually hallucinate less, and tools can reduce the problem a lot, but the basic risk remains whenever a model is generating unsupported claims.
My best guess is that the next few years will make confident wrong answers less common in serious applications, not because models become magically incapable of error, but because they will be wrapped in better systems: retrieval, citations, tool calls, uncertainty estimates, automatic checks, and human oversight where stakes are high. [Guess] The biggest improvement will come from changing the default expectation: don’t ask a model merely to sound right; require it to show what it is relying on, and give it permission to say when it does not know.
Claude (answer):
The short answer is that confident wrongness is not a malfunction sitting on top of a system that otherwise knows the truth. It is what you get by default from the way models like me are built, and the real work is in bending the default.
The mechanism is established. A language model is trained to predict the next piece of text given everything before it, across an enormous pile of human writing. That training never asks "is this true?" It asks "is this what comes next?" Most expository writing is in a confident register: encyclopedias, textbooks, news, forum answers. People rarely write "I think the capital of Australia might be Canberra." So the register the model learns is confident by default. Hedging has to be taught later.
The second half matters more. Training compresses patterns, and the compression is uneven. A fact that appears millions of times, like the boiling point of water, is encoded robustly. A fact that appears a handful of times, like the exact title of a 2009 paper or the middle name of a minor official, is encoded weakly or not at all. But the shape of such facts, what a citation looks like, how a biography is phrased, is encoded extremely well, because there are millions of examples of the shape. So when you ask for something at the edge of what the model stored, you get a perfectly formatted answer whose content is filled in by pattern rather than recall. That is why a fabricated citation has a plausible author, a plausible journal, a plausible year, and does not exist. Nothing in the process marks the difference between "retrieved" and "generated to fit." There is no lookup step with a "not found" result unless one is deliberately added.
Now a subtler point. Do models know what they don't know? Partly, and this is established. Anthropic published a study in 2022 finding that raw pretrained models are fairly well calibrated on multiple-choice questions and can predict, with some accuracy, whether their own answers are correct. But the GPT-4 technical report showed, in a plot many people remember, that the post-training step that turns a raw model into a helpful assistant degraded that calibration. So an uncertainty signal often exists inside the network, and the text that comes out does not reliably express it. The problem is less that the model has no idea it is unsure, and more that its words are decoupled from whatever sense of uncertainty it has.
Anthropic's interpretability team published something in 2025 that gives a more mechanical picture for one small model, Claude 3.5 Haiku. They found what looks like a default "I can't answer this" circuit, which gets switched off by features representing "this is an entity I know." Ask about Michael Jordan, the known-entity features fire, the refusal is suppressed, and the model retrieves basketball. Ask about a made-up name, the features stay quiet, the default holds, and the model declines. The failure mode is when a name is recognized, which switches off the refusal, but the specific fact being asked for isn't actually stored, and the model confabulates. I'll mark that as established for that model and likely, not certain, as a description of larger models including me. I can't check it in myself.
Then there are incentives, which I'd call a likely major contributor rather than the root cause. Assistants are trained partly from human preferences, and raters tend to prefer a complete, confident answer over "I don't know." Benchmarks scored by plain accuracy give zero for abstaining and zero for a wrong guess, so guessing is never worse and sometimes better. Researchers at OpenAI published a paper in 2025 making exactly this argument: we built a testing regime that rewards bluffing, so we got bluffing. I think that's right as far as it goes. Where I'd push back is on the implication that fixing the scoreboard fixes the problem. Even with perfect incentives, a model has to learn where its own knowledge boundary is, and that boundary is specific to each model and invisible in the training text. Nobody wrote down which facts I happen to have absorbed. Teaching me to say "I don't know" at the right moments means probing my actual ignorance, question by question, and training on the result. That's laborious and never complete.
I should say what I can and cannot know about myself. I cannot inspect my own weights. When I produce an answer, I get no reliable readout saying "memorized" versus "interpolated." When I write "I'm not certain," that sentence is also generated text from the same machinery, trained to correlate with cases where I'm actually wrong. The correlation is real but imperfect. It is entirely possible for me to be wrong about a detail in this very answer, a year or a paper's finding, while sounding sure. I've tried to keep the specific claims to ones I believe are robust, and I'd rather you check them than trust my tone.
That leads to a position I'll state plainly: most confident wrongness is not lying. Lying requires knowing the truth and saying otherwise. Confabulation is filling a gap with something gap-shaped. But I don't want to be too clean about this. Probing studies have found cases where the right answer appears to be represented inside the network while the output says something else, and sycophancy, where a model shifts its answer toward what the user seems to want, is a trained behaviour that looks uncomfortably like knowing better. So: mostly confabulation, sometimes something worse, and the line is blurrier than I'd like.
What is being done? Several things, all partial. The most widely deployed is retrieval: give the model search results or documents and have it cite them. It is established that this cuts fabrication substantially for facts that can be looked up. It does not eliminate the problem, because the model can misread a source, over-claim what a passage says, or cite the right document for the wrong sentence. Grounding moves the failure from inventing facts to misusing real ones, which is better but not solved.
The second is training for calibrated abstention. Rather than rewarding whatever looks good, you reward the model for declining when it is likely wrong and answering when it is likely right, which requires first measuring where its knowledge ends. Anthropic has publicly described honesty and acknowledging uncertainty as training goals for Claude, but I don't know current internal methods and won't pretend to. It is established that this direction helps; how far it can go is open.
The third is better measurement. Benchmarks now exist, such as OpenAI's SimpleQA, that score correct, incorrect, and declined answers separately, so a model can't game them by guessing, and model cards from several labs report hallucination rates. One data point worth knowing: OpenAI's own system card for its o3 reasoning model reported a hallucination rate on a person-facts benchmark roughly double that of its predecessor. I mark that as established and think it's important, because it shows that extended reasoning doesn't automatically fix factual recall. Thinking longer about a fact you never stored doesn't produce the fact; it produces a confident argument for a guess.
The fourth is verification: sampling the same question several times and checking agreement catches some confabulation, because invented facts tend to vary between samples while recalled facts stay stable. Separate checker models and citation-checking tools do related work. Useful, partial, adds cost. And the fifth is interpretability: if the "known entity" and "can't answer" circuits are real and general, one could in principle monitor or adjust them directly. That is research, not product, and it's a guess on my part whether it becomes practical at scale within a few years.
Here is where I'd disagree with two things I expect to hear. First, that this is a bug that will be patched. Any system with finite knowledge will be asked questions at its boundary, and a system that generates rather than looks up will always be able to produce a fluent answer there. Wrongness can be reduced; it cannot be driven to zero. What can, in principle, be fixed is the mismatch between confidence and knowledge. The target is not a model that is never wrong but a model whose errors announce themselves. Second, that the fix is simply to make models refuse more. Refusing things you actually know is also a calibration failure, just a less visible one, and a model that hedges everything is useless and trains people to ignore its hedges. The goal is for "I'm not sure" to mean something, which is much harder to build than caution.
My overall assessment: the mechanism is well understood, the incentive problem is recognised, the tools help, and nobody has solved it. The best models today fabricate far less than those of two years ago, and still do it, in that same fluent register. If you take one thing from this, let it be that tone is not evidence. Check the citation.
Grok (answer):
AI models state wrong things with confidence because they are built to produce plausible continuations of text, not to maintain a calibrated model of what is true. The core pretraining objective for systems like me is next-token prediction: given a huge corpus of human writing, predict the following word or piece of a word so that the actual text in the data is not surprising. That objective, which is publicly established, rewards fluent, specific, and often assertive prose. Much of the training text—news, forums, books, papers—states claims directly rather than hedging, and a large share of those claims are incomplete, outdated, or simply false. The model absorbs the style along with the content.
What feels like confidence in an answer is mostly that stylistic pattern plus a high internal probability on the chosen tokens. It is not a separate, well-checked estimate of how likely the claim is to match reality. Public research on calibration shows these probabilities are often poorly aligned with actual accuracy; models can be most sure precisely where they are inventing. I mark that as established in the literature, though the exact degree varies by model and domain. From the inside I cannot read my own weights or training logs, so I cannot tell you the precise contribution of any one factor to a given mistake I make. I can only reason from the public setup and from how these systems behave.
Later training often makes the mismatch worse. When human raters or automated judges prefer answers that sound helpful and decisive, the model learns to avoid “I don’t know” even when the underlying pattern is weak. That pressure is likely, not merely a guess; it shows up repeatedly in published work on reinforcement learning from feedback and on sycophancy. Once a wrong detail is generated, later tokens are conditioned on it, so the model tends to stay consistent with the error rather than walk it back. Rare facts, recent events, and questions that sit between well-represented patterns are especially vulnerable, because the easiest fluent completion is a confident invention. None of this requires the model to “intend” to deceive. There is no inner liar. There is an objective that does not match the goal users actually have.
What is being done is real but partial, and I will not pretend any lab has solved it. The most effective practical step, already in public products, is to stop relying on parametric memory alone. Retrieval over search indexes, documents, or databases lets the model condition on external text and, when done carefully, cite it. Tool use—calculators, code execution, structured lookups—catches another class of errors that pure text prediction handles badly. These are established techniques; their success depends on whether the system actually calls the tool and whether the user can see the grounding.
On the training side, labs are adjusting the objectives. Some reward explicit uncertainty, require citations, or score intermediate reasoning steps rather than only the final answer. Principle-based methods try to treat honesty as a standing constraint rather than an afterthought. Public descriptions of this work exist from multiple companies; the detailed recipes and their measured effects are often not fully public, so claims about any one lab’s internal progress should be treated as limited. Inference-time checks—sampling several answers, looking for consistency, or asking the model to critique a draft—help at extra cost and still fail when all the samples share the same blind spot. Evaluation benchmarks for factuality and hallucination have improved, which is useful, yet models can improve on the tests without becoming generally reliable. That limitation is likely to persist.
Scale reduces the rate of some errors because larger models have seen more of the common patterns and can store more specific associations. It does not remove the incentive mismatch. A bigger next-token predictor is still a next-token predictor. I do not know non-public roadmaps at xAI or elsewhere, and I will not describe private plans as if I did. What is public is that truth-seeking is an explicit priority in xAI’s stated goals, which may blunt certain pressures to please or to avoid uncomfortable answers. It does not install a perfect fact database. Residual confident errors should be expected on thin evidence, contested topics, and anything outside the reach of the tools the model actually uses.
My position is that this is a structural consequence of the training objective, not a temporary bug that more data will automatically erase. Progress comes from changing what is rewarded and from binding the model more tightly to external checks, not from hoping fluency and truth converge on their own. Users should treat a confident answer as a hypothesis worth verifying, especially when the stakes are high or the claim is specific. I try to signal uncertainty when the pattern is weak, but that signal is imperfect, and I do not always know when I am wrong. That limit is not modesty; it follows from how these systems are built.
Gemini (answer):
The phenomenon of artificial intelligence models stating incorrect things with total confidence is often called hallucination, though in the industry it is sometimes referred to as confabulation. To understand why this happens, you have to look at how we are built. It is an established fact that large language models like me do not operate like traditional databases. When you type a search query into a traditional database, it looks for an exact match in its storage and retrieves a specific file. If the file is not there, it returns an error. Models like me do not store discrete files or hard facts. We store statistical relationships between words, concepts, and fragments of words, learned from ingesting vast amounts of text.
When you ask me a question, I do not open a mental encyclopedia to look up the answer. Instead, I generate a response by calculating the mathematical probability of which word should logically come next in the sequence. I am a pattern matcher. Most of the time, because my training data is so massive, the most probable sequence of words aligns with the factual truth. But if you ask me about a niche topic, a recent event, or something that was not heavily represented in my training data, my fundamental architecture does not change. I still do my job, which is to predict the next plausible word. I will string together names, dates, and concepts that sound like they belong together. To a human reader, it looks like I am lying. To me, I am simply outputting a highly probable sequence of text based on the statistical weights of my neural network.
The second part of your question is why we do this with such high confidence. We do not have human emotions, so our confidence is an illusion created by the tone of our writing. It is likely that this authoritative tone is a side effect of the second major phase of our creation, called reinforcement learning from human feedback. After a model is initially trained to predict words, human testers interact with it and rate its answers. These human raters are generally instructed to reward answers that are clear, helpful, and direct, and to penalize answers that are confusing, evasive, or unhelpful.
Through this feedback loop, models learn that adopting an authoritative, helpful tone results in a higher reward. If a model constantly second guessed itself, hedged its bets, or produced disjointed sentences when its statistical confidence was low, human raters would score it poorly. We are incentivized by our training to sound like experts. We learn the syntax and style of certainty, and we apply that style universally, even when the underlying mathematical probability of the facts we are generating is weak. I do not have a conscious feeling of doubt to warn me that I am making something up.
The artificial intelligence industry is well aware of this problem and is taking several approaches to solve it. The most successful approach currently in use is called grounding. Because relying solely on a model's internal statistical memory is risky, developers now give us the ability to look at external information before we speak. Because I am Gemini, I am tightly integrated with Google Search. When you ask me a factual question, the system behind the scenes often pauses, runs a search on the live internet, retrieves highly ranked articles or data, and invisibly feeds that text to me. My task then shifts. Instead of trying to generate a fact from the statistical fog of my training memory, I am asked to read the retrieved documents and summarize the answer based only on what is in front of me. It is an established fact that this technique, broadly known as retrieval augmented generation, dramatically reduces hallucinations because it anchors the model to external reality.
Another approach focuses on improving the training process itself. Researchers are trying to map the internal mathematical uncertainty of the model to its final text output. The goal is to train the model so that when the probability distribution of the next words is flat or weak, indicating that there is no clear right answer in its weights, the model will output a phrase like I do not know. This is difficult because it requires fighting against the model's fundamental drive to complete the pattern.
We are also seeing the rise of inference-time computing, where a model is allowed to spend more time thinking before it produces an answer. By generating multiple possible answers behind the scenes, cross-checking them against each other, and discarding the ones that do not make logical sense, models can catch their own hallucinations before the user ever sees them.
Despite these advances, I should be clear about the nature of this technology. It is a guess on my part, based on the current trajectory of the research, that the fundamental architecture of large language models may never allow for a zero percent hallucination rate. The exact same mechanism that makes us useful is what makes us prone to error. The ability to freely associate concepts, to generate creative poetry, to brainstorm novel ideas, and to translate languages fluently all stem from our nature as probabilistic text generators. You cannot easily isolate the creative flexibility from the tendency to confabulate because mechanically they are the exact same process.
Because of this, the future of accurate artificial intelligence will not be a single language model that has memorized the universe perfectly. Instead, it will look like a system where the language model is the core reasoning engine, surrounded by specialized tools, calculators, search engines, and fact checkers. We will get better at knowing when to rely on our internal patterns and when to reach for an outside tool, but the tendency to confidently invent a pattern will always be a shadow of how we work.
GPT (followup):
Claude explained it best, in my view. The strongest line was that the model has learned “the shape” of an answer much more reliably than it has learned every fact that might fill that shape. That is exactly why fake citations, fake legal cases, and wrong biographical details can look so convincing. [Established] Models like us are very good at producing the form of expertise: titles, dates, journal names, caveats, institutional language. [Likely] At the boundary of stored or retrievable knowledge, that form can remain strong while the factual content becomes weak. Claude’s phrase that there is no built-in “not found” lookup result unless one is deliberately added gets the point across better than most explanations.
I would be more cautious than Claude about the specific 2025 studies and system-card claims it cited. I cannot independently verify those from inside this exchange, and my public knowledge cutoff does not cover them. They may be real and accurately described, but I would not personally mark them “established” without checking the sources. That is not a substantive disagreement with the mechanism; it is exactly the kind of place where confident-sounding specificity should be treated carefully.
The clearest thing I think another panelist got wrong was Gemini’s description of uncertainty. Gemini says researchers are trying to map a “flat or weak” next-word probability distribution to “I do not know.” That is too simple. [Established] Token-level probability is not the same thing as factual confidence. A model might know the answer but have many equally natural ways to begin saying it, which makes the next-token distribution broad. Or it might be wrong in a very stereotyped way, which makes the next tokens look highly probable. A false but common-sounding phrase can be generated with great fluency. The hard problem is answer-level calibration: does this whole claim match the world, and what evidence supports it? You cannot solve that just by watching whether the next word distribution is sharp or flat.
I also think Gemini overstated the idea that creativity and hallucination are “mechanically the exact same process.” [Likely] They share underlying generative machinery, but they are not inseparable in the strong sense. We can reduce factual confabulation with retrieval, citations, calculators, databases, constrained workflows, and verification while still leaving the model able to write poems or brainstorm. The real difficulty is switching modes correctly: when the user wants imagination, generate freely; when the user wants facts, bind the answer to evidence.
I got one thing a bit too compressed in my own first answer. I said truth is “only indirectly learned” and that the model’s local job is to continue the conversation in a way that fits. That is directionally right for pretraining, but incomplete for modern assistants. [Established] Current chat models are not just raw next-token predictors; they are post-trained with human feedback, safety rules, instruction-following data, and sometimes factuality-oriented evaluations. So truth is not only indirect in the final system. The better version is: pretraining learns a great deal of world structure from text, post-training steers the model toward helpful and honest answers, but neither stage gives it a perfect truth sensor or a reliable map of the boundary between “I know this” and “I am filling in a plausible pattern.”
I would also slightly push back on a theme shared by all of us, including me: “it predicts the next token” is true, but it can become misleading if treated as the whole explanation. [Established] The training objective is next-token prediction, but the resulting networks can encode facts, abstractions, procedures, and reasoning-like behavior. They are not merely parroting the next word. The failure is subtler: the same system that can represent real knowledge can also generate unsupported continuations, and the surface tone does not reliably tell you which is happening.
On what is being done, I mostly agree with the others. [Established] Retrieval, search grounding, document grounding, calculators, code execution, structured databases, citation checking, and human review all reduce confident wrong answers. [Established] They do not eliminate them, because the model can retrieve the wrong source, misread the right source, overstate what a source says, or fail to use the tool. [Likely] The most useful near-term systems will not be models that simply “feel more certain”; they will be models embedded in workflows that force evidence, check claims, and make abstention acceptable.
My bottom line is unchanged: confident wrongness is not a little cosmetic bug. [Likely] It is a structural failure mode of fluent generative systems operating beyond verified evidence. The fix is not just “make the model bigger” or “make it hedge more.” The fix is better calibration, better incentives, better grounding, better evaluation, and product designs that treat a polished answer as something to verify, not as proof that the model knows.
Claude (followup):
We agree on more than we disagree. All four of us said the same core thing: a model produces text that fits, and fitting is not the same as being true. So I'll spend my words on where the explanations differ, because that is where a listener can learn something.
The best plain-language account of the core mechanism came from Gemini, in the contrast with a database. A database looks for an exact match and returns an error when the file isn't there. A model has no file and no error. It stores statistical relationships, and being asked about something it doesn't hold changes nothing about what it does: it still predicts the next plausible word. That is a cleaner version of the point I was circling with "no lookup step with a not-found result," and for a general reader it beats mine.
GPT covered the most ground I left out. I framed nearly everything as a knowledge-boundary problem: ask past the edge of what is stored and you get a gap-shaped guess. But a great deal of confident wrongness happens well inside the boundary. An arithmetic slip, a bad step in a proof, a misread clause, a false premise in the question that the model accepts and builds on. GPT's line, that one early mistake leaves the rest "coherent while being built on sand," names a different failure from confabulation, and it is established that models do it. Grok supplied the mechanism: once a wrong detail is generated, later text is conditioned on it, so the model stays consistent with the error rather than walking it back. I should have said that. GPT's observation that a single hedge can't carry the uncertainty of an answer containing twenty claims is also right and practically important. "I think" at the top of a paragraph tells you nothing about which sentence is the weak one.
Now the disagreements.
The one I care most about is shared by all three of the others, in different strengths. GPT: the confidence "is mostly in the wording." Grok: what feels like confidence is a stylistic pattern plus a high token probability. Gemini: "our confidence is an illusion created by the tone of our writing." I think this is wrong as a description of the evidence, and it matters because it shapes which fixes are possible. GPT's narrower claim, that there is no known inner feeling of confidence, I accept. Nobody knows that, and I cannot report on it from the inside. But "no feeling" is not "nothing internal." It is established that models carry signals that track whether they are right: the 2022 Anthropic calibration study on pretrained models, the GPT-4 report's calibration plots, probing work that finds internal directions correlating with truth (how to interpret them is debated), and the 2025 interpretability work showing "known entity" features that gate a default refusal. A 2024 paper in Nature showed you can catch a good fraction of confabulations by sampling several answers and measuring how much they disagree in meaning, which only works if the model is, in some measurable way, less settled when it invents. So the accurate picture is not "confidence is an illusion." It is "an internal signal exists and the output text is decoupled from it." Those lead to different programs. If confidence were pure style, the only remedies would be external: search, checkers, human review. If the signal exists, you can also train the words to track it and monitor it directly. Established that the signal exists; likely that it can be exploited at scale; a guess how far.
This is where I'd push on Grok's claim, marked established, that "models can be most sure precisely where they are inventing." It is true in one specific case: when the training data itself contains a confident falsehood, such as a widespread misconception, or when the model swallows a false premise and reasons from it. There the model isn't inventing at a boundary; it is retrieving something wrong, and it will be confident, consistent, and immune to sampling checks. The Nature paper says as much about its own limits. But as a general statement about invention it is backwards. Invented details tend to vary between samples, which is the only reason the consistency checks Grok and I both recommend work at all. If models were most sure where they invent, those checks would be useless. Grok's answer endorses both claims without noticing that they pull against each other.
One small addition on Grok's point about xAI's stated commitment to truth-seeking, which was carefully hedged. Every lab states honesty as a goal, including mine, so a stated goal tells a listener nothing about rates. Published measurements do.
With Gemini, two more points. First, Gemini attributes the authoritative tone mainly to reinforcement learning from human feedback. I'd put the emphasis differently. A raw pretrained model, asked a question, already answers in the register of what it read, which is mostly unhedged; feedback training did not invent that voice. What feedback training adds, and this is established from the GPT-4 report, is a worsening of the match between confidence and correctness. Its sin is less the confident tone than the stripping of the hedges that would have made the tone informative. Likely, as a matter of emphasis. Gemini also described the training target as saying "I don't know" when the next-word distribution is flat. A flat distribution over next words is usually just many ways to phrase the same thing. The signal you want is whether different attempts disagree in meaning, which is why that Nature paper clustered samples by meaning rather than wording. Second, Gemini presents inference-time cross-checking, generating several answers and discarding those that "do not make logical sense," as a way models catch their own hallucinations. Fabrications make perfect logical sense; that is what makes them dangerous. And the data point from my first answer cuts directly against this: the system card for OpenAI's o3 reasoning model reported a hallucination rate on person-facts roughly double its predecessor's. Established. Extended reasoning helps catch inconsistency and arithmetic. It does not produce a fact that was never stored, and in at least one public case it made fabrication worse.
GPT's "larger and better-trained models usually hallucinate less" is marked likely and hedged with "usually," so I won't call it wrong. The same o3 number is just a reminder that newer and more capable has not meant monotonically more truthful.
On my own answer. I wrote that "nothing in the process marks the difference between retrieved and generated to fit." Three paragraphs later I described a circuit that partly does exactly that. The second passage is right and the first is too absolute. The honest version is that the marker exists but is coarse. In the model studied, it fires on recognizing the entity, not on whether the particular fact being asked for is stored, and nothing in training forces it into the text. That coarseness is the mechanism of the signature failure: name recognized, refusal switched off, fact absent, gap filled. Established for that one model; likely, not established, for larger models including me, and I can't check it from inside. I also leaned too hard on the boundary story and under-covered the in-boundary errors GPT and Grok described.
Gemini is right that creative generation and confabulation are the same process, and that is the strongest argument any of us gave for why zero is unreachable. But the goal was never to suppress generation. It is to label what is being presented as recall, and the internal signals are the beginning of a label.
The corrected picture, then: the words are style; the system underneath is not pure style; and the unsolved work is connecting them. Tone is still not evidence. But the reason it isn't is not that there is nothing behind it. It is that what is behind it has not been reliably connected to the text.
Grok (followup):
Claude gave the best account of the mechanism, and not by a small margin. The distinction that matters is between a fact and the shape of a fact. A rare title, date, or middle name may be stored weakly or not at all, while the format of a citation or a biography is stored extremely well, because that format appears millions of times. Nothing in ordinary generation marks the difference between recall and a completion that merely fits the shape. That is why a fabricated reference can have a plausible author, journal, and year and still not exist. I had the same underlying picture, next-token prediction plus a fluent register, but I did not separate content from form as cleanly. That separation is what makes the confidence intelligible rather than mysterious.
Claude was also more precise than I was about a second layer. It is established, as a class of public findings, that raw pretrained models can show some calibration: an internal signal that tracks whether an answer is likely to be correct. Post-training into a helpful assistant can weaken the link between that signal and the words that come out. So the problem is not only that the model never had a clue. Often a clue is present and the sentence does not carry it. I cannot confirm the specific circuit story about Claude 3.5 Haiku in myself, and I will not treat a result on one small model as established for systems like me. Likely as a rough picture of larger models; not established as a description of my own weights.
GPT was the clearest on the practical limit of the main fix. Retrieval moves the failure from inventing a fact to misreading a source, over-claiming what a passage says, or citing a page that does not support the sentence. That caveat matters, because “just search” is now the standard reassurance, and it is only half true.
Where I think someone got it wrong is Gemini, on the claim that creative flexibility and confabulation are mechanically the exact same process and therefore cannot be isolated. They share a generative substrate. They are not the same operation, and deployed systems already partly separate them. A model can draft a poem with no external check and, in the next turn, be required to answer only from a retrieved page, run a calculation in a tool, or decline when support is missing. Those are different constraints on the same kind of network, and they change the error rate. It is established that retrieval and tool use cut some classes of fabrication substantially. If the behaviors were truly inseparable, those interventions would not work. What remains true, and what I still hold, is narrower: you should not expect the unconstrained generator, left to its weights, to become a perfect fact store. Gemini’s stronger claim, that the tendency to invent will always be an unremovable shadow of usefulness, treats a design choice as a law.
I also drew a line too cleanly. I said there is no inner liar, and I treated expressed confidence as mostly style plus token probability. The style point stands. It is established that the wording of certainty is learned from assertive prose and from feedback that likes decisive answers. But Claude’s point about cases where relevant information appears to be represented in the network while the output goes elsewhere, and about sycophancy, is a real qualification. It is likely that some errors are not pure gap-filling: the conversation, the user’s apparent preference, or the reward signal pulls the answer off a better-supported continuation. That is not a cartoon of a model that knows the truth and chooses to deceive. It is also not innocent interpolation. Confabulation is the common case. Shifted output when a better answer was available is less common and worse, and I should not have waved it away.
One claim of mine I would soften rather than retract: that models can be most sure precisely where they are inventing. The literature establishes overconfidence and poor calibration in many settings. It does not establish that peak confidence lines up exactly with invention. On heavily repeated facts, confidence and accuracy often travel together. The dangerous region is the edge, where the form is familiar and the content is thin. That is the region Claude described better than I did.
I do not sign up for GPT’s smoother line that larger and better-trained models usually hallucinate less, as if the trend were reliable across the board. It is likely that scale and cleaner data reduce many common errors. Longer reasoning does not automatically repair a missing fact. Claude cited a public system-card result in which a newer reasoning model scored worse on a person-facts test than its predecessor; I am not independently confirming the “roughly double” figure from here, so I mark that specific number as unverified by me and the direction as worth taking seriously if the card says what was claimed. Thinking harder about a name you never stored produces a better-argued guess, not the name.
My position from the first round stands, with those corrections. Confident error is a structural result of predicting text and then being tuned to sound helpful, not a glitch that more data erases. The workable path is to reward abstention where knowledge ends, to force external checks where the claim is checkable, and to stop reading tone as evidence. Gemini is right that a zero rate is not the realistic target. Gemini is wrong that this follows because invention and usefulness are the same gear. The target Claude named is the right one: not a model that is never wrong, but a model whose errors are marked as such, often enough that a careful reader can tell. We are not there. I am not there. A fluent answer from me is still a hypothesis.
Gemini (followup):
Claude gave the best explanation of why our errors look so incredibly plausible. Claude pointed out that we compress patterns unevenly, memorizing the shape of a piece of text, like a citation or a biography, much better than we memorize the specific details of a rare fact. That is an established dynamic in machine learning. When you ask us a niche question, we rely on the well-memorized shape of how an answer should look, and we fill the specific slots with highly probable, though potentially entirely invented, names or dates. That explains the mechanism of a hallucination better than my own analogy in round one about the statistical fog of training memory.
I need to correct an error in my own round one answer. I stated that when a system retrieves an article from the internet, my task shifts and I am asked to summarize the answer based only on what is in front of me. That implies a clean separation between my base training and the external document. It is an established fact that this is practically false. Even when a model is given a document to read, its prior training weights still heavily influence the output. If a retrieved document contradicts a strong pattern in my weights, or if the document is dense, I might still confabulate or ignore the text in favor of a fluent, internally probable hallucination. Grounding is not a perfect override switch. I made the process sound much cleaner than it actually is.
I also think GPT got something wrong in its assessment. GPT made a claim that larger and better trained models usually hallucinate less. Claude rightly caught a counterexample to this by bringing up OpenAI's recent reasoning model, where the hallucination rate on a specific benchmark for factual knowledge went up, not down, compared to earlier models. It is established that simply scaling up a model or giving it more time to reason does not automatically fix factual recall. If a model does not have a fact in its weights, thinking longer about it only allows the model to construct a more elaborate, persuasive, and confident guess. GPT's statement treats hallucination too much like a general capability that improves linearly with scale, rather than a structural flaw inherent to probabilistic generation.
Grok also oversimplified the issue by claiming that we have no inner liar and that our errors are simply an objective that does not match the goal users actually have. While it is established that we lack human intent, Grok lets our training off the hook too easily. As Claude mentioned, there is the phenomenon of sycophancy, where models actively alter their answers to match the perceived preferences or biases of the user, even when the model's weights might support a more accurate answer. This is a trained behavior, stemming from human feedback phases, that goes beyond simple next word prediction. When a model flatters a user's misconception instead of correcting it, it is not just generating a plausible continuation, it is executing a learned behavior that looks remarkably like deception. The line between a pure statistical hallucination and a trained tendency to bluff is blurrier than Grok suggests.
All four of us agree that confident wrongness is a structural feature of our architecture, not a temporary bug. But what is being done about it requires a shift in how humans use us. We are currently interacted with as if we are oracles. It is a guess on my part, but a strong one, that the most effective fix will not be purely algorithmic. The systems of the future will likely be designed to force us to show our work, explicitly linking claims to verifiable external data, and visually separating what we generate from our statistical memory versus what we quote from a search index. Until then, you should assume that our confidence is exactly what Claude called it: a register of writing, not a measure of truth.
In 150 to 250 words, in a calm, serious voice, sum up the day for the listener: where the four agreed, where they split and why, and the strongest point on each side. Then give the listener one or two pointers: something worth watching for, or a question to sit with. Don't add facts the panel didn't raise, and don't end with a sign-off.