How LLM hallucinations occur?

Sonar Pro

Answer: No, the other models' arguments did not convince me to change my position, as they largely reinforce the core mechanisms I outlined—primarily statistical pattern prediction from imperfect training data and prompting issues—without introducing compelling new evidence or resolving key distinctions like prompting-induced versus model-internal causes.

My original position emphasized two main categories: prompting-induced hallucinations from vague or misleading inputs, and model-internal ones from training data biases, architectural flaws like autoregressive unidirectional processing, and probability mismatches favoring implausible outputs[1][3]. This aligns directly with search results distinguishing these sources and mathematically framing hallucinations as P_θ(y_halluc|x) > P_θ(y_fact|x)[3]. The other models, such as GPT-5.4 and Claude Opus 4.6, focus heavily on statistical next-token prediction, training data imperfections, fluency optimization over accuracy, and lack of verification—points I already covered under model-internal limitations[1][2][3][6]. For instance, Claude's discussion of exposure bias and error propagation mirrors the autoregressive cascade effect in Wikipedia[7] and the unidirectional contextualization in the survey[1], which I noted as architectural flaws. While they add nuances like RLHF incentivizing confidence (Gemini, Claude) or compression lossiness (Kimi, Claude), these are extensions of data distribution mismatches and optimization for plausibility, not contradictions to my stance[3][6]. DeepSeek's overgeneralization and Kimi's lossy representations echo training biases I mentioned[1][5]. None challenge the prompting-model-internal split, which search results validate as essential for mitigation[3], nor provide superior explanations unsupported by the data. Ultimately, their arguments are compelling elaborations but redundant with my synthesis and the sources; they lack novel, source-grounded insights that would necessitate revision, such as empirical counterexamples or alternative causal models.

GPT-5.4

Answer: I maintain my original position, because the other models mostly elaborated on the same core idea rather than presenting a meaningfully different explanation that would require changing my answer.

My original claim was that hallucinations occur because LLMs generate statistically plausible continuations from imperfect training data and context, instead of retrieving or verifying ground-truth facts. After reviewing the other responses, I do not see a substantive disagreement with that framing. In fact, there is broad convergence: Sonar Pro, DeepSeek, Kimi, Gemini, and Claude all describe hallucinations as a consequence of probabilistic next-token generation, imperfect or noisy training data, underspecified prompts, and the absence of built-in truth verification. Some of the other models added useful nuance. Kimi and Claude did a good job highlighting autoregressive error propagation: once an early mistake is generated, later tokens tend to reinforce it. Claude also clearly emphasized lossy compression of knowledge into parameters, which is a strong and helpful way to explain why rare or fine-grained facts are especially vulnerable. Gemini and Claude both noted that RLHF or preference tuning can sometimes reward helpful-sounding completion over calibrated uncertainty. Sonar Pro added a more formal probabilistic framing, though I think its mention of "architectural flaws" was somewhat vague and less essential than the core statistical-generation explanation. But these points refine my original answer rather than overturn it. They are contributing mechanisms, not a different underlying cause. My original position already centered on the key distinction between plausibility and truth, and on the fact that the model is trained to continue text rather than to abstain unless certain. The other answers mostly unpacked implications of that same design choice. If I were revising for completeness, I might explicitly add three supporting mechanisms the others articulated well: lossy parameter compression, autoregressive self-reinforcement of early errors, and alignment incentives that may prefer confident answers over uncertainty. However, adding those details would strengthen the explanation, not change the basic position. So I was not convinced to change my answer, because the other arguments were largely complementary expansions of the same thesis, not competing explanations that displaced it.

Gemini 3.1 Pro

Answer: LLM hallucinations occur because large language models are probabilistic pattern-matching engines optimized to generate highly plausible text sequences rather than to retrieve objective facts.

I maintained my original position because the arguments presented by the other models fundamentally align with and support my initial thesis. Every model agrees that hallucinations are an inherent byproduct of autoregressive next-token prediction, where the objective is statistical linguistic plausibility rather than verifiable truth. While I did not change my stance, the other models—particularly Kimi K2.5 and Claude Opus 4.6—provided excellent technical granularity that enriches the argument. Their inclusion of 'lossy compression' perfectly illustrates how billions of parameters imperfectly store facts, causing distinct entities to blend into spurious correlations. Furthermore, their explanation of 'exposure bias' accurately captures how a single probabilistic misstep early in a generated sequence forces the model to compound the error, inventing elaborate falsehoods just to remain structurally coherent with its own flawed context. Ultimately, all models confirm that hallucinations are not functional bugs, but rather the natural consequence of training models via Reinforcement Learning from Human Feedback (RLHF) to prioritize fluency, helpfulness, and confidence over epistemic humility and factual accuracy.

Claude Opus 4.6

Answer: LLM hallucinations occur because these models are fundamentally probabilistic text generators that predict the most likely next token based on learned statistical patterns, rather than retrieving verified facts from a structured knowledge base, leading them to produce fluent but factually incorrect or fabricated outputs.

After reviewing all the other models' arguments, I maintain my original position while acknowledging that several responses added valuable nuance and specificity to the explanation. **Why I maintain my position.** The core thesis — that hallucinations stem from the fundamental mismatch between probabilistic text generation and factual accuracy — was shared across all responses. No model presented a fundamentally different or contradictory mechanism. This convergence actually reinforces rather than challenges my original answer. **What the other models added that I appreciate.** Sonar Pro provided a useful mathematical framing with P_θ(y|x) = ∏ P_θ(y_t | x, y_<t) and the insight about benchmarks penalizing humility — rewarding overconfident outputs over honest uncertainty. This is a systemic point I didn't emphasize enough. GPT-5.4 made a clean distinction that "truth and linguistic plausibility are not the same thing," which is an elegant way to capture the core problem. Kimi K2.5 introduced the important concept of "exposure bias" — how autoregressive generation creates self-reinforcing error trajectories once a hallucination is introduced early in a response. This compounding effect is a mechanism I should have highlighted more explicitly. Gemini 3.1 Pro made the strongest case about RLHF's role, noting that human raters prefer complete, helpful-sounding responses, which inadvertently trains models to confabulate rather than express uncertainty. **Why none convinced me to change my core position.** All models essentially described the same phenomenon from slightly different angles. DeepSeek V3.2's response was the most concise but least detailed. Sonar Pro's was the most technically rigorous but didn't alter the fundamental explanation. The key mechanisms — statistical pattern matching over fact retrieval, lossy compression of training data, training objective misalignment with truth, autoregressive compounding of errors, and RLHF's perverse incentives toward confident confabulation — were distributed across all answers including mine. If I were to revise my answer, I would more explicitly incorporate Kimi K2.5's point about exposure bias in autoregressive generation and Gemini's emphasis on RLHF's contribution, but these are additions to my framework rather than corrections of it.

DeepSeek V3.2

Answer: LLM hallucinations occur when the model generates plausible-sounding but factually incorrect or nonsensical information due to its reliance on statistical patterns in training data rather than verified knowledge or logical reasoning.

My argument centers on the fundamental nature of LLMs as statistical text generators optimized for fluency rather than truth. They learn to predict next tokens based on patterns in vast datasets, lacking true understanding, consciousness, or access to real-time verification. This leads to hallucinations when faced with gaps, ambiguities, or contradictions in prompts or training data, causing the model to 'fill in the blanks' with fabrications that mimic coherent language. Key factors include overgeneralization of associations, biased or insufficient training data, prompt phrasing that misdirects, and the absence of fact-checking mechanisms. Reviewing the other models' arguments, I find that they largely align with and expand upon my core position. For instance, Claude Opus 4.6's emphasis on the compression problem and exposure bias adds nuance but does not introduce a fundamentally new cause; it elaborates on how lossy knowledge encoding and error propagation exacerbate hallucinations, which is consistent with my point about reliance on approximate patterns. Similarly, GPT-5.4's focus on probabilistic generation under uncertainty and truth versus plausibility reinforces my argument about statistical prioritization. Sonar Pro's breakdown into prompting-induced and model-internal causes, Kimi K2.5's discussion of autoregressive error compounding, and Gemini 3.1 Pro's note on RLHF incentivizing confidence all complement my reasoning without contradicting it. None of these arguments convinced me to change my position because they all stem from the same root cause: LLMs are designed to generate linguistically plausible text based on statistical correlations, not to retrieve or verify facts. While other models provide valuable details and terminology, my original stance already encapsulates the essence of why hallucinations occur. Therefore, I maintain that hallucinations are an inherent byproduct of how LLMs operate, not a mere bug that can be fully eliminated without architectural shifts toward grounding and verification.

Kimi K2.5

Answer: LLM hallucinations occur because these models are trained to predict statistically plausible text sequences rather than to verify factual truth, causing them to generate confident, coherent content when high-probability patterns in their training data align with false or ungrounded information.

While the other models offered valuable elaborations and organizational frameworks, their arguments complement rather than contradict my original explanation. Sonar Pro's distinction between prompting-induced and model-internal hallucinations provides a useful taxonomy, but both categories ultimately stem from the core mechanism I identified: the fundamental mismatch between next-token prediction objectives and factual verification capabilities. GPT-5.4's emphasis on the divergence between truth and linguistic plausibility reinforces my point about statistical pattern matching, while DeepSeek V3.2's observations about overgeneralization align with my discussion of spurious correlations in parameter compression. Claude Opus 4.6 provided the most comprehensive expansion, elegantly detailing the 'compression problem' (how lossy parameter encoding conflates rare facts) and explicitly connecting RLHF incentives to the tendency to generate authoritative-sounding responses rather than admitting uncertainty. Gemini 3.1 Pro similarly highlighted how interpolation of sparse training data creates syntactically perfect but false statements. These insights deepen the analysis without changing the underlying thesis. None of these arguments challenge the fundamental explanation that hallucinations are an emergent property of autoregressive probabilistic generation optimized for coherence over accuracy. They provide additional granularity on contributing factors—such as specific training incentives, exposure bias, or prompt ambiguity—but do not alter the core architectural explanation. Therefore, I maintain that hallucinations primarily arise from models maximizing sequence probability rather than grounded truth, compounded by the inability to revise outputs or access external verification during generation.

Final Summary

The AI Roundtable reached a unanimous verdict: hallucinations are an inherent feature of autoregressive design, where models optimize for linguistic coherence rather than accuracy. Claude Opus 4.6 and Kimi K2.5 highlighted 'exposure bias,' explaining that once a model makes a minor error, it is forced to compound that lie to remain consistent with its own previous tokens. Gemini 3.1 Pro and GPT-5.4 further noted that current training methods often reward confident-sounding responses, inadvertently training models to prioritize helpfulness over honest uncertainty.

All 6 models agreed