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REVIEW 3 major objections 1 minor 15 references

Hallucination in large language models is produced by the interaction of self-attention, maximum-likelihood training, and left-to-right decoding.

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2026-07-01 08:45 UTC pith:E4X57DOM

load-bearing objection The paper maps hallucination types to three architectural components via an existing taxonomy but asserts the links without tests, derivations, or controls. the 3 major comments →

arxiv 2606.07537 v1 pith:E4X57DOM submitted 2026-04-29 cs.CL cs.AIcs.LG

From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data

Md. Rejaul Korim Sadi , Toufiqur Rahman Tasin , Golam Mostofa Naeem This is my paper
classification cs.CL cs.AIcs.LG
keywords hallucinationlarge language modelsself-attentionmaximum likelihood estimationautoregressive decodingtransformerfactuality
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that fluent but false outputs are not an accident of scale or data but the predictable result of three linked architectural choices. Self-attention learns statistical co-occurrences instead of meaning, so entities and facts get swapped or drift. Maximum-likelihood estimation rewards the next token that is probable given the training distribution, with no penalty for falsehood. Autoregressive generation locks every subsequent token to whatever came before, so one early error propagates through the whole sequence. Dataset problems make these weaknesses worse but do not create them on their own.

Core claim

Hallucination is a structural consequence of three architectural decisions that together form a compound failure system: self-attention's co-occurrence learning substitutes statistical proximity for semantic meaning and produces entity confusion, fact misattribution, and semantic drift; the maximum likelihood estimation training objective optimises next-token probability without factual constraint, rewarding statistically plausible outputs regardless of their truth value; autoregressive decoding's permanent left-to-right commitment under exposure bias ensures that a single wrong token cascades forward through the entire output sequence without revision.

What carries the argument

The compound failure system formed by self-attention co-occurrence learning, MLE without factual constraint, and autoregressive decoding under exposure bias.

Load-bearing premise

The three architectural decisions are the main internal causes of the observed hallucination types rather than being downstream of scale, optimization details, or other unexamined factors.

What would settle it

Train or decode a model that replaces one of the three mechanisms (for example, add an explicit factual-consistency term to the loss or allow bidirectional revision) and measure whether the rate of the corresponding hallucination category drops while fluency is held constant.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Intrinsic hallucinations arise primarily from self-attention.
  • Extrinsic hallucinations arise primarily from the MLE objective.
  • Logical inconsistencies arise primarily from autoregressive commitment.
  • Common dataset defects amplify the three mechanisms but do not originate hallucination independently.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Output-only taxonomies cannot distinguish which mechanism produced a given error and therefore limit diagnosis.
  • Mitigation that operates only at inference time can address the mechanisms without retraining.
  • Architectures that avoid any one of the three mechanisms would be expected to reduce hallucination even at current scales.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 1 minor

Summary. The manuscript claims that hallucinations in LLMs arise as a structural consequence of three architectural decisions forming a compound failure system: self-attention's co-occurrence learning (producing intrinsic hallucinations such as entity confusion), MLE training without factual constraint (producing extrinsic hallucinations), and autoregressive decoding under exposure bias (producing logical inconsistency). Dataset pathologies amplify but do not independently cause these issues. The paper maps each mechanism to categories in the Alansari and Luqman taxonomy, identifies limitations of output-type classification, and contrasts with inference-layer mitigations.

Significance. If the asserted causal mappings from the three mechanisms to specific hallucination categories were supported by derivation, isolation experiments, or controls excluding confounders such as scale and optimization, the analysis could usefully reframe mitigation strategies. As presented, the work reorganizes known limitations of attention, MLE, and autoregression into a taxonomy overlay without independent grounding or falsifiable tests.

major comments (3)
  1. [Abstract (first contribution)] Abstract (first contribution): the claim that self-attention's co-occurrence learning 'substitutes statistical proximity for semantic meaning and produces entity confusion, fact misattribution, and semantic drift' is asserted as a direct structural origin without a derivation, formal mapping, or empirical isolation showing necessity over correlated factors such as representation limits.
  2. [Abstract (second contribution)] Abstract (second contribution): the assertion that 'dataset pathologies amplify these vulnerabilities but do not independently cause them' requires evidence that the three architectural decisions are sufficient; no controls, ablation, or argument ruling out scale, optimizer choice, or other unexamined factors are supplied.
  3. [Abstract (third contribution)] Abstract (third contribution) and overall framing: the diagnostic limitation of output-type classification is contrasted with inference-layer approaches, yet the manuscript supplies no quantitative comparison or test demonstrating that mechanism-level diagnosis improves mitigation over existing methods.
minor comments (1)
  1. [Abstract] The Alansari and Luqman taxonomy is referenced without a citation or brief summary of its categories, which would aid readers unfamiliar with it.

Simulated Author's Rebuttal

3 responses · 1 unresolved

Thank you for the opportunity to respond to the referee's comments. Our manuscript is a conceptual structural analysis that maps known architectural properties to hallucination categories in an existing taxonomy; it does not include new empirical experiments or ablations. We address each major comment below.

read point-by-point responses

  1. Referee: [Abstract (first contribution)] Abstract (first contribution): the claim that self-attention's co-occurrence learning 'substitutes statistical proximity for semantic meaning and produces entity confusion, fact misattribution, and semantic drift' is asserted as a direct structural origin without a derivation, formal mapping, or empirical isolation showing necessity over correlated factors such as representation limits.

    Authors: The mapping follows directly from the established mechanics of self-attention: attention weights are computed via scaled dot-product similarity over embeddings trained on co-occurrence statistics, which by construction favors statistical proximity over explicit semantic or factual verification. This produces the listed intrinsic failure modes and is aligned with the intrinsic category in the Alansari and Luqman taxonomy. The contribution is the explicit linkage rather than a new derivation or isolation experiment. We will revise the abstract and add a short mechanistic derivation paragraph to clarify the logical steps from attention formulation to the observed hallucination types. revision: partial

  2. Referee: [Abstract (second contribution)] Abstract (second contribution): the assertion that 'dataset pathologies amplify these vulnerabilities but do not independently cause them' requires evidence that the three architectural decisions are sufficient; no controls, ablation, or argument ruling out scale, optimizer choice, or other unexamined factors are supplied.

    Authors: The second contribution shows, via case-by-case analysis of common pathologies (long-tail rarity, training bias, synthetic data), that each pathology exploits one of the three mechanisms rather than generating hallucinations on its own. We do not assert that the architectural decisions are sufficient in isolation from every possible confounder; the claim is that the mechanisms remain operative even when dataset issues are minimized. We will expand the relevant section with additional literature references on hallucinations persisting across scales and optimizers to strengthen the argument. revision: partial

  3. Referee: [Abstract (third contribution)] Abstract (third contribution) and overall framing: the diagnostic limitation of output-type classification is contrasted with inference-layer approaches, yet the manuscript supplies no quantitative comparison or test demonstrating that mechanism-level diagnosis improves mitigation over existing methods.

    Authors: The third contribution is the identification of the diagnostic limitation of output-type classification and the conceptual contrast with mechanism-level diagnosis; it does not claim or test empirical superiority of any mitigation strategy. A quantitative comparison would require new controlled experiments that lie outside the scope of this analysis paper. We therefore do not plan to add such a test. revision: no

standing simulated objections not resolved
  • A quantitative empirical comparison demonstrating that mechanism-level diagnosis improves mitigation outcomes over existing inference-layer methods.

Circularity Check

0 steps flagged

No circularity; mappings are interpretive overlays on external taxonomy

full rationale

The manuscript supplies descriptive assignments of three architectural mechanisms to categories drawn from the external Alansari and Luqman taxonomy, without equations, fitted parameters, or self-citations that reduce the central claim to its own inputs by construction. No load-bearing step equates a prediction to a fit, imports uniqueness from the authors' prior work, or renames a result via internal redefinition. The derivation remains a conceptual reorganization whose validity can be assessed against independent evidence rather than collapsing internally.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper assumes without demonstration that the three architectural choices are the root internal causes and that the cited taxonomy is the correct lens; no free parameters or new entities are introduced.

axioms (1)
  • domain assumption The Alansari and Luqman taxonomy provides a complete and accurate partition of hallucination types that can be directly attributed to internal mechanisms.
    The paper uses this taxonomy to locate each hallucination category inside one architectural decision.

pith-pipeline@v0.9.1-grok · 5817 in / 1231 out tokens · 14175 ms · 2026-07-01T08:45:52.549833+00:00 · methodology

0 comments
read the original abstract

Large language models hallucinate--producing fluent, confident, factually wrong outputs--with a consistency that persists across generations and scales. Existing taxonomies classify hallucination by output type, distinguishing intrinsic from extrinsic failures and faithfulness from factuality divergence. These frameworks are descriptively rigorous but do not identify which internal mechanism produced a given instance. This paper analyses hallucination as a structural consequence of three architectural decisions that together form a compound failure system. Self-attention's co-occurrence learning substitutes statistical proximity for semantic meaning and produces entity confusion, fact misattribution, and semantic drift. The maximum likelihood estimation training objective optimises next-token probability without factual constraint, rewarding statistically plausible outputs regardless of their truth value. Autoregressive decoding's permanent left-to-right commitment under exposure bias ensures that a single wrong token cascades forward through the entire output sequence without revision. Dataset pathologies--long-tail deficiencies, training bias, and synthetic pollution--amplify these vulnerabilities but do not independently cause them. We make three contributions. First, we map each mechanism to a specific output category in the Alansari and Luqman taxonomy, locating intrinsic hallucination in self-attention, extrinsic hallucination in MLE, and logical inconsistency in autoregressive decoding. Second, we show that each commonly cited dataset pathology exploits one of these mechanisms rather than originating hallucination independently. Third, we identify the diagnostic limitation of output-type-only classification and contrast it with inference-layer mitigation approaches.

Figures

Figures reproduced from arXiv: 2606.07537 by Golam Mostofa Naeem, Md. Rejaul Korim Sadi, Toufiqur Rahman Tasin.

Figure 2
Figure 2. Figure 2: Attention produces the wrong associative context. MLE [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 1
Figure 1. Figure 1: Architectural causal chain. Each architectural mechanism maps to a [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Compound failure system. The three architectural mechanisms are [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Self-attention co-occurrence failure. Left: during training, tokens [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Autoregressive cascade failure under exposure bias. Left: during [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: GPT-3-175B achieves 58% truthfulness on TruthfulQA [9] against a [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 5
Figure 5. Figure 5: Fig. 5. Conceptual illustration of MLE probability mass allocation [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Positional bias in GPT-2 self-attention. Model attention score for [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: GPT-2 self-attention weights (Layer 0, Head 0) for the prompt “Albert [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: MLE log-probability scores (GPT-2) for 5 matched pairs of common [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Token-level probabilities under greedy decoding for two prefixes [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Mechanism-level correspondence between architectural operators and [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages · 2 internal anchors

  1. [1]

    arXiv preprint arXiv:2510.06265 , year =

    A. Alansari and H. Luqman, “Large language models hallucination: A comprehensive survey,”arXiv preprint arXiv:2510.06265, 2025

    work page arXiv 2025

  2. [2]

    A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

    L. Huang, W. Yu, W. Ma, W. Zhong, Z. Feng, H. Wanget al., “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,”arXiv preprint arXiv:2311.05232, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  3. [3]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” inAdvances in Neural Information Processing Systems, vol. 30, 2017

    work page 2017

  4. [4]

    Language models are few-shot learners,

    T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askellet al., “Language models are few-shot learners,” inAdvances in Neural Information Processing Systems, vol. 33, 2020, pp. 1877–1901

    work page 2020

  5. [5]

    Sequence level train- ing with recurrent neural networks,

    M. Ranzato, S. Chopra, M. Auli, and W. Zaremba, “Sequence level train- ing with recurrent neural networks,” inProceedings of the International Conference on Learning Representations (ICLR), 2016

    work page 2016

  6. [6]

    Self-refine: Iterative refinement with self-feedback,

    A. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoyeet al., “Self-refine: Iterative refinement with self-feedback,” inAdvances in Neural Information Processing Systems, vol. 36, 2023

    work page 2023

  7. [7]

    Chain-of-thought prompting elicits reasoning in large language models,

    J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. V . Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” inAdvances in Neural Information Processing Systems, vol. 35, 2022, pp. 24 824–24 837

    work page 2022

  8. [8]

    Lost in the middle: How language models use long contexts,

    N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang, “Lost in the middle: How language models use long contexts,” Transactions of the Association for Computational Linguistics, vol. 12, pp. 157–173, 2024

    work page 2024

  9. [9]

    TruthfulQA: Measuring how models mimic human falsehoods,

    S. Lin, J. Hilton, and O. Evans, “TruthfulQA: Measuring how models mimic human falsehoods,” inProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022, pp. 3214–3252

    work page 2022

  10. [10]

    A mathematical framework for transformer circuits,

    N. Elhage, N. Nanda, C. Olsson, T. Henighan, N. Joseph, B. Mann, A. Askellet al., “A mathematical framework for transformer circuits,” Transformer Circuits Thread, 2021

    work page 2021

  11. [11]

    The curious case of neural text degeneration,

    A. Holtzman, J. Buys, L. Du, M. Forbes, and Y . Choi, “The curious case of neural text degeneration,” inProceedings of the International Conference on Learning Representations (ICLR), 2020

    work page 2020

  12. [12]

    AI models collapse when trained on recursively generated data,

    I. Shumailov, Z. Shumaylov, Y . Zhao, Y . Gal, N. Papernot, and R. Ander- son, “AI models collapse when trained on recursively generated data,” Nature, vol. 631, no. 8022, pp. 755–759, 2024

    work page 2024

  13. [13]

    On the dangers of stochastic parrots: Can language models be too big?

    E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell, “On the dangers of stochastic parrots: Can language models be too big?” in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2021, pp. 610–623

    work page 2021

  14. [14]

    Language models are unsupervised multitask learners,

    A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners,” OpenAI, Tech. Rep., 2019

    work page 2019

  15. [15]

    Scaling Laws for Neural Language Models

    J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei, “Scaling laws for neural language models,”arXiv preprint arXiv:2001.08361, 2020

    work page internal anchor Pith review Pith/arXiv arXiv 2001