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REVIEW 3 major objections 5 minor 1 cited by

Span-Level Hallucination Detection for LLM-Generated Answers

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper claims that hallucinated spans in LLM answers can be detected by decomposing each answer into semantic roles, scoring each role's entailment against retrieved context, and thresholding the combined score at 0.5.

desk verdict A real flaw in the confidence equation empties the central claim, but the Arabic parser comparison is a decent empirical data point. read the letter →

arxiv 2504.18639 v1 pith:PSL7GR6S submitted 2025-04-25 cs.CL

Passant Elchafei , Mervet Abu-Elkheir This is my paper · ORCID
classification cs.CL
keywords hallucinationdetectionspan-levelsemanticrolelabelingtextualentailmenttokenconfidenceArabicNLPMu-SHROOMSemEval-2025
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims that hallucinated spans in LLM-generated answers can be localized by decomposing the answer into atomic semantic roles, retrieving a reference context with GPT-4, and scoring each role with a DeBERTa textual entailment model. It blends the entailment probability with a token-level confidence score computed from the model's output logits, then labels any semantic unit whose refined score falls below 0.5 as hallucinated. On the Mu-SHROOM shared-task data, the framework reports an IoU of 0.358 and a correlation of 0.322 for English, and an IoU of 0.28 for Arabic when dependency parsing precedes semantic role labeling. The motivation is that sentence-level or token-level detectors miss which exact span is unsupported, while role-level units give a localizable target for correction and fact-checking.

What carries the argument

The central object is the refined score per semantic unit, $\text{refined\_score} = \alpha \cdot \text{entailment} + (1-\alpha) \cdot \text{confidence}$, thresholded at 0.5 to mark hallucinated spans. The entailment component comes from a DeBERTa model classifying each SRL-extracted unit against the retrieved context; the confidence component is Eq. (1), defined as the average over the unit's $n$ tokens of $e^{\logit_i}/\sum_j e^{\logit_j}$. The framework's other load-bearing machinery is the decomposition step: a BERT-based SRL model for English, and either a multilingual SRL model or CamelParser2.0 dependency parsing followed by SRL for Arabic, which create the atomic units that make span-level localization possible.

What would settle it

Recompute Eq. (1) for two units of the same length whose logits are opposite extremes: the confidence scores are identical, so the term cannot reflect token-level uncertainty; rerunning the pipeline with a per-token softmax average would change refined scores and the reported IoU if the confidence component were doing any work.

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Extended reading notes

Core claim

On the paper's own account, hallucination is a property of semantic roles, not whole sentences: an LLM answer is decomposed into predicate-argument units, each unit is compared with a GPT-4-retrieved reference context using a DeBERTa entailment model, and the entailment probability is blended with a token-level logit confidence score to give a refined score per unit. Units whose refined score falls below 0.5 are labeled hallucinated spans. The paper reports that this pipeline achieves an IoU of 0.358 and a correlation of 0.322 on English Mu-SHROOM data, and that Arabic detection improves from IoU 0.205 with a multilingual SRL model to 0.28 when CamelParser dependency parsing is applied first. A separate LLM fact-checking step confirms 83% of the English hallucinated spans with GPT-4 and 72% with LLaMA.

Load-bearing premise

The load-bearing premise is that the logit-based confidence score measures how uncertain the model was about each token, even though the formula makes that score equal to one divided by the number of tokens in the unit.

Editorial extensions

If this is right

  • A unit whose refined score falls below 0.5 is treated as hallucinated, so the framework turns a continuous entailment/confidence signal into an explicit, inspectable span decision.
  • English detection reaches IoU 0.358 and correlation 0.322 on Mu-SHROOM, so predicted spans overlap the annotated hallucinated spans at a level that supports span-level use.
  • Arabic detection improves from IoU 0.205 with multilingual SRL alone to IoU 0.28 when CamelParser dependency parsing precedes SRL, implying that syntactic structure helps hallucination detection in morphologically rich languages.
  • Independent LLM fact-checking confirms 83% of English hallucinated spans with GPT-4 and 72% with LLaMA, indicating that the detected spans align with external factual judgment.
  • Because hallucinations are flagged per semantic unit, a downstream system could rewrite or correct only the offending span rather than the whole answer.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • One consequence the paper leaves implicit: Eq. (1) evaluates to one divided by the number of tokens in the unit, so the confidence term carries no per-token uncertainty and refined-score differences come from the entailment term and unit length alone.
  • A testable replacement, not proposed in the paper, is to use the mean per-token softmax probability as the confidence term and re-measure IoU on the same data; unchanged scores would show the logit component is inert, while changed scores would reveal that unit length was the real driver.
  • An extension the paper does not draw: the Arabic gain from adding dependency parsing before SRL (IoU 0.205 to 0.28) suggests that parser choice, not the entailment model, is the binding constraint for morphologically rich languages.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper presents a span-level hallucination detection system for the SemEval-2025 Mu-SHROOM shared task. The method decomposes LLM-generated answers into semantic roles via Semantic Role Labeling, retrieves a reference context through GPT-4 prompting, scores each role with a DeBERTa-based textual entailment model, and combines the entailment score with a token-level logit-based confidence score (Eq. 1) into a refined score (Eq. 2). Units with refined score below 0.5 are marked as hallucinated. Experiments on English and Arabic report IoU 0.358/Cor 0.322 and IoU 0.28/Cor 0.21 respectively, with additional GPT-4/LLaMA verification of detected spans.

Significance. If the proposed confidence refinement were valid, the paper would provide a practical, language-aware approach to span-level hallucination detection that combines semantic decomposition with model uncertainty. The SRL-based decomposition and the comparison of two Arabic parsing pipelines are useful engineering contributions. However, the central confidence mechanism is mathematically degenerate, no baseline or ablation is reported, and the evaluation has a self-referential component. As a result, the scientific claims are not currently supported; the paper is better viewed as a system description that needs substantial correction.

major comments (3)
  1. [§4.3, Eq. (1)] The confidence score is degenerate. Because the softmax denominator sums over the same n tokens as the numerator, the average of the n probabilities is identically 1/n regardless of the logit values. The paper's own examples confirm this: 'a silver medal' yields logit_score 0.3333 and the 9-token 'in the 2008 Summer Olympics in Beijing , China' yields 0.1111, exactly 1/n. Consequently Eq. (2) reduces to refined_score = α·entailment + (1−α)/n, and the confidence component carries no information about token-level uncertainty. The abstract and §4.5 credit this term with refining the entailment scores, but that claim is unsupported by the equation as written. The authors must correct the normalization (e.g., define the denominator over the model's output vocabulary or over a fixed token set) or remove the confidence component entirely and re-evaluate the system.
  2. [§5.2–5.3] The evaluation is not reproducible and does not support the claimed contribution. The hyperparameter α and the procedure for setting the refined-score threshold of 0.5 are not reported; the worked example in §5.2 implicitly suggests α ≈ 0.6 (if the entailment probability is taken as 0.007), but this is never stated. There is no ablation that removes the confidence term, no comparison to an entailment-only baseline or to other Mu-SHROOM systems, and no error bars or significance tests. Given that Eq. (1) is degenerate, an ablation is essential to determine whether the integrated score outperforms the entailment score alone. Without these, the reported IoU/Cor values in §5.3 cannot be attributed to the claimed mechanism.
  3. [§4.1 and §5.3] The use of GPT-4 for both context retrieval and the fact-checking verification step introduces a self-referential evaluation. Because the same model provides the reference context and then judges whether a span is hallucinated, shared systematic biases could inflate the agreement rates (83% English, 58% Arabic). The manuscript should at least discuss this limitation and ideally verify a subsample with an independent retrieval-backed fact-checker.
minor comments (5)
  1. [§6] The Arabic Cor is reported as 2.1 in the conclusion but 0.21 in §5.3; since a correlation coefficient cannot exceed 1 in magnitude, the conclusion should be corrected.
  2. [§5 heading and throughout] There are several typos: 'Expriments' should be 'Experiments', 'mertrics' should be 'metrics', 'algirthm' should be 'algorithm', and 'languauge' should be 'language'.
  3. [§2] The phrase 'hallucination detection at the spectral level' appears to be a typo for 'span-level'.
  4. [§3.1] The Arabic example strings appear garbled due to font/encoding issues; please ensure the Unicode text renders correctly in the final PDF.
  5. [§4 and Figure 1] The text references Figure 1 and its workflow but does not walk through the figure's components; a short step-by-step explanation would improve readability.

Circularity Check

1 steps flagged · score 6.0 of 10

Equation (1)'s logit confidence score is 1/n for every atomic unit, so the claimed confidence refinement is vacuous and the combined-score mechanism is definitionally unsupported.

  1. self definitional [Section 4.3, Equation (1); applied in Section 4.5, Equation (2); example in Section 5.2]
    "logit_score = 1/n \sum_{i=1}^n e^{logit_i} / \sum_j e^{logit_j} (1) ... The final logit score is the average softmax probability of tokens in the generated output. ... 'logit_score': 0.3333333333333333 ... 'logit_score': 0.1111111111111111"

    The denominator sums over the same n tokens whose numerator terms are averaged. Therefore \sum_i e^{logit_i} / \sum_j e^{logit_j} = 1 identically for any logits, forcing logit_score = 1/n. The paper's own worked examples confirm this: a 3-token unit receives exactly 1/3 and a 9-token unit exactly 1/9, values that depend only on token count. Consequently Equation (2) reduces to refined_score = alpha * entailment + (1-alpha) / n, so the claimed 'token-level confidence measures derived from output logits' carry no logit-dependent uncertainty information. The abstract's central claim that entailment scores are 'further refined through token-level confidence measures derived from output logits' is thus definitionally vacuous, and the reported IoU/Cor values cannot validate that mechanism.

full rationale

Most of the pipeline is externally grounded: the Mu-SHROOM dataset supplies ground-truth labels, SRL decomposes answers into units, and the DeBERTa entailment model is an independent pretrained component. The confidence-scoring step, however, is definitionally empty. In Equation (1), the same n tokens appear in both the numerator and the denominator, so the expression is the constant 1/n for every atomic unit; the logits themselves cancel. The paper's own outputs (0.333... for the 3-token unit and 0.111... for the 9-token unit) are exactly the length-only values. This makes Equation (2) a weighted average of the entailment score and a token-count constant, so the framework's advertised combination of semantic and probabilistic confidence is not actually implemented. The undisclosed alpha hyperparameter and the use of GPT-4 for both retrieval and fact-checking are reproducibility/validity concerns rather than circular derivations; no load-bearing self-citations or fitted parameters renamed as predictions were found. Because the central refinement claim reduces by construction to a token-count constant, a partial-circularity score of 6 is appropriate.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim rests on two untested domain assumptions (GPT-4-retrieved context as a reliable reference; SRL units aligning with hallucination spans) and one mathematical premise (Eq. (1) yielding a confidence measure) that is contradicted by the paper's own examples. There are no invented entities. Two numeric hyperparameters appear without tuning details.

free parameters (2)
  • alpha
    Weight in Eq. (2) balancing entailment and confidence; no value or tuning search is reported.
  • refined score threshold = 0.5
    Chosen in Section 5.2 as 'a balanced decision point' with no analysis or ablation.
assumptions (3)
  • domain assumption GPT-4-retrieved context is a reliable factual reference for evaluating answers.
    Section 4.1 states the retrieved context 'is expected to contain key facts' but no accuracy check of the retrieved contexts is reported.
  • domain assumption Semantic roles from SRL are atomic units suitable for hallucination span detection.
    Section 4.2 uses SRL to split answers into components; no analysis relates role boundaries to annotation spans.
  • ad hoc to paper The softmax-style normalization in Eq. (1) yields a token confidence measure.
    With the denominator summing over all tokens, logit_score equals 1/n, which the examples in Section 5.2 confirm. The premise is therefore contradicted by the paper's own output.

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Cite this review

Pith. "Pith review of Span-Level Hallucination Detection for LLM-Generated Answers." pith.science (2026). https://pith.science/paper/PSL7GR6S

@misc{pith2026250418639,
  author       = {Pith},
  title        = {Pith review of: Span-Level Hallucination Detection for LLM-Generated Answers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PSL7GR6S}},
  note         = {Machine review of arXiv:2504.18639}
}
read the original abstract

Detecting spans of hallucination in LLM-generated answers is crucial for improving factual consistency. This paper presents a span-level hallucination detection framework for the SemEval-2025 Shared Task, focusing on English and Arabic texts. Our approach integrates Semantic Role Labeling (SRL) to decompose the answer into atomic roles, which are then compared with a retrieved reference context obtained via question-based LLM prompting. Using a DeBERTa-based textual entailment model, we evaluate each role semantic alignment with the retrieved context. The entailment scores are further refined through token-level confidence measures derived from output logits, and the combined scores are used to detect hallucinated spans. Experiments on the Mu-SHROOM dataset demonstrate competitive performance. Additionally, hallucinated spans have been verified through fact-checking by prompting GPT-4 and LLaMA. Our findings contribute to improving hallucination detection in LLM-generated responses.

Figures

Figures reproduced from arXiv: 2504.18639 by the authors.

Figure 1
Figure 1. Span-Level Hallucination Detection Framework [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Example of Arabic sentence SRL extraction [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A comprehensive taxonomy of hallucinations in Large Language Models

    cs.CL 2025-08 conditional novelty 2.0 of 10

    A survey that organizes LLM hallucination types, causes, benchmarks, and mitigations, and restates the theorem that hallucination is inevitable for computable LLMs.

Reference graph

Works this paper leans on

23 extracted references · 8 canonical work pages · cited by 1 Pith paper

  1. [1]

    Hazem Abdelazim, Tony Begemy, Ahmed Galal, Hala Sedki, and Ali Mohamed. 2024. Multi-hop arabic llm reasoning in complex qa. Procedia Computer Science, 244:66--75

    work page 2024

  2. [2]

    Zhihong Chen, Feng Jiang, Junying Chen, Tiannan Wang, Fei Yu, Guiming Chen, Hongbo Zhang, Juhao Liang, Chen Zhang, Zhiyi Zhang, et al. 2023. Phoenix: Democratizing chatgpt across languages. arXiv preprint arXiv:2304.10453

    arXiv 2023

  3. [3]

    Ahmed Elshabrawy, Muhammed AbuOdeh, Go Inoue, and Nizar Habash. 2023. Camelparser2. 0: A state-of-the-art dependency parser for arabic. In Proceedings of ArabicNLP 2023, pages 170--180

    work page 2023

  4. [4]

    Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, et al. 2024. Fact-checking the output of large language models via token-level uncertainty quantification. arXiv preprint arXiv:2403.04696

    arXiv 2024

  5. [5]

    Han He and Jinho D. Choi. 2021. https://aclanthology.org/2021.emnlp-main.451 The stem cell hypothesis: Dilemma behind multi-task learning with transformer encoders . In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5555--5577, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics

    work page 2021

  6. [6]

    Pengcheng He, Jianfeng Gao, and Weizhu Chen. 2021. Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing. arXiv preprint arXiv:2111.09543

    arXiv 2021

  7. [7]

    Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12):1--38

    2023

  8. [8]

    Fuxiao Liu, Kevin Lin, Linjie Li, Jianfeng Wang, Yaser Yacoob, and Lijuan Wang. 2023. Mitigating hallucination in large multi-modal models via robust instruction tuning. In The Twelfth International Conference on Learning Representations

    2023

Show all 23 references
  1. [9]

    Tianyu Liu, Yizhe Zhang, Chris Brockett, Yi Mao, Zhifang Sui, Weizhu Chen, and Bill Dolan. 2021. A token-level reference-free hallucination detection benchmark for free-form text generation. arXiv preprint arXiv:2104.08704

    2021 arXiv

  2. [10]

    Junliang Luo, Tianyu Li, Di Wu, Michael Jenkin, Steve Liu, and Gregory Dudek. 2024. Hallucination detection and hallucination mitigation: An investigation. arXiv preprint arXiv:2401.08358

    2024 arXiv

  3. [11]

    Hamdy Mubarak, Hend Al-Khalifa, and Khaloud Suliman Alkhalefah. 2024. Halwasa: Quantify and analyze hallucinations in large language models: Arabic as a case study. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and E...

    2024

  4. [12]

    Yifu Qiu, Yftah Ziser, Anna Korhonen, Edoardo M Ponti, and Shay B Cohen. 2023. Detecting and mitigating hallucinations in multilingual summarisation. arXiv preprint arXiv:2305.13632

    2023 arXiv

  5. [13]

    Ernesto Quevedo, Jorge Yero, Rachel Koerner, Pablo Rivas, and Tomas Cerny. 2024. Detecting hallucinations in large language model generation: A token probability approach. arXiv preprint arXiv:2405.19648

    2024 arXiv

  6. [14]

    The halogen benchmark: Fantastic llm hallucinations and where to find them

    Abhilasha Ravichander, Shrusti Ghela, David Wadden, and Yejin Choi. The halogen benchmark: Fantastic llm hallucinations and where to find them

  7. [15]

    Ferial Senator, Abdelaziz Lakhfif, Imene Zenbout, Hanane Boutouta, and Chahrazed Mediani. 2025. https://doi.org/10.1109/ACCESS.2025.3525493 Leveraging chatgpt for enhancing arabic nlp: Application for semantic role labeling and cross-lingual annotation projection . IEEE Access...

    2025

  8. [16]

    Peng Shi and Jimmy Lin. 2019. Simple bert models for relation extraction and semantic role labeling. arXiv preprint arXiv:1904.05255

    2019 arXiv

  9. [17]

    Abdellah Hamouda Sidhoum, M’hamed Mataoui, Faouzi Sebbak, and Kamel Sma \" li. 2022. Acqad: a dataset for arabic complex question answering. In International conference on cyber security, artificial inteligence and theoretical computer science

    2022

  10. [18]

    A stitch in time saves nine: Detecting and mitigating hallucinations of llms by actively validating low-confidence generation

    Neeraj Varshney, Wenlin Yao, Hongming Zhang, Jianshu Chen, and Dong Yu. A stitch in time saves nine: Detecting and mitigating hallucinations of llms by actively validating low-confidence generation

  11. [19]

    Ra\'ul V\'azquez, Timothee Mickus, Elaine Zosa, Teemu Vahtola, J\"org Tiedemann, Aman Sinha, Vincent Segonne, Fernando S\'anchez-Vega, Alessandro Raganato, Jindřich Libovický, Jussi Karlgren, Shaoxiong Ji, Jindřich Helcl, Liane Guillou, Ona de Gibert, Jaione Bengoetxea, Joseph...

    2025

  12. [20]

    David Wadden, Kyle Lo, Bailey Kuehl, Arman Cohan, Iz Beltagy, Lucy Lu Wang, and Hannaneh Hajishirzi. 2022. Scifact-open: Towards open-domain scientific claim verification. arXiv preprint arXiv:2210.13777

    2022 arXiv

  13. [21]

    Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xiangru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Wenyang Gao, Xuming Hu, Zehan Qi, et al. 2023. Survey on factuality in large language models: Knowledge, retrieval and domain-specificity. arXiv preprint arXiv:2310.07521

    2023 arXiv

  14. [22]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  15. [23]

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