arxiv: 2605.08462 · v1 · submitted 2026-05-08 · 💻 cs.CL · cs.AI

Recognition: 1 theorem link

· Lean Theorem

Do Benchmarks Underestimate LLM Performance? Evaluating Hallucination Detection With LLM-First Human-Adjudicated Assessment

I. F. Atasoy , B. Mutlu , E. A. Sezer , A. Wahdan

Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:14 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords hallucination detectionLLM evaluationbenchmark reliabilityhuman annotationsummarizationadjudicationcontextual hallucination

0 comments

The pith

Re-adjudicating conflicted samples with two humans raises measured LLM hallucination detection accuracy on summarization benchmarks.

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

The paper tests whether standard benchmarks for spotting contextual hallucinations in LLM-generated summaries understate model performance because of errors in their original single-pass human labels. It runs GPT and Gemini predictions on the QAGS-C and SummEval datasets, flags every sample where the model disagrees with the original label, and sends those conflicts to two independent cross-cultural human adjudicators for review. After the re-evaluation, agreement among the adjudicators, GPT, and Gemini rises by 6 to 8 percent, the models' accuracy on the corrected labels increases, and the adjudicators side with the LLMs more often when the models supply explicit reasoning. The work concludes that single-annotator labels are too noisy for this ambiguous task and that LLM-assisted re-evaluation produces more reliable ground truth.

Core claim

Following re-evaluation of all conflicted samples through a human adjudication process involving 2 cross-cultural adjudicators, triple agreement between human, GPT, and Gemini increased by 6.38% for QAGS-C and 7.62% for SummEval. Model accuracy improved, with GPT increasing by 4.25% on QAGS-C and 2.34% on SummEval, while Gemini showed gains of 8.51% and 3.80%, respectively. Adjudicators frequently sided with the models' judgments over original human annotations when LLMs provided explicit reasoning. Overall human adjudicator agreement ranged between 83% and 87%. These findings suggest that for ambiguity-prone tasks, single-pass annotations may be insufficient, and model-assisted re-evalution

What carries the argument

LLM-first detection of annotation conflicts followed by targeted human adjudication on the disagreed samples.

If this is right

Single-pass annotations are insufficient for ambiguity-prone tasks such as hallucination detection in summarization.
LLM-assisted re-evaluation of conflicts produces more reliable benchmarks.
Models with explicit reasoning often receive higher agreement from human adjudicators than the original labels.
Reported performance of LLMs on these benchmarks increases once the ground truth is refined.

Where Pith is reading between the lines

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

The same conflict-flagging and re-adjudication method could reduce noise in other subjective NLP evaluation tasks.
Human labels may not always be the definitive reference when models supply traceable reasoning.
Benchmarks could adopt routine LLM pre-screening as a standard step before final annotation.

Load-bearing premise

The two cross-cultural adjudicators produce a more accurate ground truth than the original single-pass annotations, and observed differences mainly reflect annotation errors rather than irreducible task ambiguity.

What would settle it

A fresh independent adjudication round on the same conflicted samples that aligns more closely with the original labels than with the re-adjudicated ones.

read the original abstract

Hallucination remains a persistent challenge in Large Language Models (LLMs), particularly in context-grounded settings such as RAG and agentic AI systems. This study focuses on contextual hallucination detection in summarization tasks. We analyze the QAGS-C and SummEval datasets by comparing original benchmark annotations with reason and span-based predictions from Gemini 2.5 Flash and GPT-5 Mini. To address systematic divergences between human labels and LLM judgments, we re-evaluated all conflicted samples through a human adjudication process involving 2 cross-cultural adjudicators. Following this re-evaluation, triple agreement (between human, GPT, and Gemini) increased by 6.38% for QAGS-C and 7.62% for SummEval. Similarly, model accuracy improved, with GPT increasing by 4.25% on QAGS-C and 2.34% on SummEval, while Gemini showed gains of 8.51% and 3.80%, respectively. Notably, adjudicators frequently sided with the models' judgments over original human annotations when LLMs provided explicit reasoning. Overall human adjudicator agreement ranged between 83% and 87%. These findings suggest that for ambiguity-prone tasks, single-pass annotations may be insufficient, and model-assisted re-evaluation yields more reliable benchmarks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Re-adjudication of conflicts in two hallucination datasets raises agreement and accuracy by a few points, but the process shows humans the model outputs first, so the gains may partly come from anchoring rather than cleaner labels.

read the letter

The paper's core finding is that LLM-guided conflict detection followed by two cross-cultural humans re-labeling the disagreements lifts triple agreement 6-8 points on QAGS-C and SummEval and lifts the models' own accuracy 2-8 points. Adjudicators sided with the models more often when the models gave explicit reasoning, and the two adjudicators agreed with each other 83-87% of the time. That is the concrete result worth noting. It is new in the specific numbers and datasets, and the method is a straightforward extension of existing disagreement-resolution practices. The work does a service by showing that single-pass annotations on these tasks leave measurable label noise and by giving a practical way to reduce it. The numbers are easy to understand and the inter-adjudicator agreement is reported, which is better than many benchmark papers manage. The main soft spot is exactly the one the stress test raises. Because the adjudicators see the span predictions and reasoning before they decide, the new labels are not independent of the models being evaluated. The paper itself says the humans frequently sided with the models in those cases, which makes it hard to separate genuine error correction from deference or anchoring. Without a blinded control arm or at least a clear description of how conflicts were sampled and whether any statistical test was run, the reported gains are difficult to interpret cleanly. Sample sizes are also missing from the abstract, so the effect sizes cannot be judged for stability. The assumption that the original divergences were mostly annotation mistakes rather than task ambiguity is plausible but not yet demonstrated. This paper is for people who maintain or rely on hallucination benchmarks for summarization and RAG. A reader who wants a concrete example of how to tighten an existing dataset will find usable ideas, even if the current numbers need more scrutiny. It is worth sending to peer review so the methods section can be checked for sampling details, blinding, and any statistical support; the central observation is worth testing properly rather than desk-rejecting.

Referee Report

3 major / 2 minor

Summary. The paper analyzes hallucination detection on the QAGS-C and SummEval summarization benchmarks by comparing original human annotations against span-based predictions and explicit reasoning from GPT-5 Mini and Gemini 2.5 Flash. It re-evaluates all conflicted samples via a two-adjudicator (cross-cultural) human process that exposes adjudicators to the LLM outputs, reporting post-adjudication gains of 6.38% and 7.62% in triple agreement, plus model accuracy lifts of 2.34–8.51%. The central claim is that single-pass annotations underestimate LLM performance due to correctable errors and that model-assisted re-adjudication produces more reliable ground truth, with overall adjudicator agreement of 83–87%.

Significance. If the central claim holds after addressing bias and reporting gaps, the work would be moderately significant for LLM evaluation research. It would demonstrate that existing hallucination benchmarks contain systematic annotation noise that depresses measured model performance and would provide a concrete, low-cost protocol (LLM-first adjudication) for improving benchmark quality in context-grounded tasks. This could influence future dataset construction in summarization, RAG, and agentic settings, provided the gains are shown to be independent of the adjudication procedure itself.

major comments (3)

[Abstract] Abstract: the reported percentage improvements (6.38% triple-agreement gain on QAGS-C, 7.62% on SummEval; 4.25% GPT accuracy gain on QAGS-C, etc.) are given without sample sizes, confidence intervals, p-values, or inter-adjudicator agreement breakdowns, preventing assessment of whether the observed changes exceed noise.
[Adjudication process] Adjudication process: adjudicators are shown the LLMs' span predictions and explicit reasoning before deciding and the paper states they 'frequently sided with the models' judgments'; this design introduces a clear risk of anchoring/deference bias, and the absence of any blinded control arm means the measured gains cannot be cleanly attributed to error correction rather than ratification of the provided LLM output.
[Results] Results / Discussion: the interpretation that original divergences reflect annotation errors (rather than irreducible task ambiguity or model-specific biases) is load-bearing for the claim that benchmarks underestimate performance, yet no analysis of ambiguous cases, disagreement patterns, or alternative explanations is supplied to support this distinction.

minor comments (2)

[Abstract] Abstract: the precise model versions, prompting templates, and span-extraction procedures for GPT-5 Mini and Gemini 2.5 Flash should be stated explicitly so that the predictions are reproducible.
Overall manuscript: full citations and version information for the QAGS-C and SummEval datasets are needed in the main text, not only in the abstract.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and have revised the manuscript to incorporate the suggested improvements where feasible.

read point-by-point responses

Referee: [Abstract] Abstract: the reported percentage improvements (6.38% triple-agreement gain on QAGS-C, 7.62% on SummEval; 4.25% GPT accuracy gain on QAGS-C, etc.) are given without sample sizes, confidence intervals, p-values, or inter-adjudicator agreement breakdowns, preventing assessment of whether the observed changes exceed noise.

Authors: We agree that the abstract and results would benefit from these statistical details. In the revision, we will report the exact number of conflicted samples that underwent re-adjudication for each dataset, include 95% bootstrap confidence intervals around the reported percentage gains, and add p-values from paired statistical tests (e.g., McNemar's test) for the accuracy improvements. We will also expand the inter-adjudicator agreement reporting with per-dataset breakdowns. These changes will be reflected in both the abstract and the main text. revision: yes
Referee: [Adjudication process] Adjudication process: adjudicators are shown the LLMs' span predictions and explicit reasoning before deciding and the paper states they 'frequently sided with the models' judgments'; this design introduces a clear risk of anchoring/deference bias, and the absence of any blinded control arm means the measured gains cannot be cleanly attributed to error correction rather than ratification of the provided LLM output.

Authors: We acknowledge the risk of anchoring bias inherent in exposing adjudicators to model outputs and reasoning. Our protocol was designed to give adjudicators full context for resolving conflicts on an ambiguity-prone task, and instructions emphasized evaluating against the source document rather than deferring to models. Nevertheless, without a blinded control arm we cannot fully isolate the source of the observed gains. In the revised manuscript we will add an explicit limitations paragraph discussing this potential bias and recommending that future benchmark improvements include blinded adjudication arms for comparison. revision: partial
Referee: [Results] Results / Discussion: the interpretation that original divergences reflect annotation errors (rather than irreducible task ambiguity or model-specific biases) is load-bearing for the claim that benchmarks underestimate performance, yet no analysis of ambiguous cases, disagreement patterns, or alternative explanations is supplied to support this distinction.

Authors: We have added a new analysis subsection that examines disagreement patterns across the conflicted samples. Cases are categorized according to whether the LLM reasoning supplied explicit, verifiable support from the source text that was not reflected in the original label, versus cases exhibiting genuine task ambiguity or potential model-specific biases. This categorization, together with a balanced discussion of alternative explanations, has been incorporated into the results and discussion sections to strengthen the evidential basis for our interpretation. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical re-labeling with observed metrics

full rationale

The paper reports results from an empirical experiment that compares original annotations to LLM predictions and then performs human re-adjudication on conflicts, measuring subsequent changes in agreement and accuracy. No equations, fitted parameters, self-citations, or ansatzes are present in the derivation chain; the increases (e.g., 6.38% triple agreement on QAGS-C) are direct observational outcomes rather than quantities that reduce to the inputs by construction. The analysis is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the empirical observation that human re-adjudication changes labels in favor of LLMs; no free parameters, new axioms, or invented entities are introduced.

pith-pipeline@v0.9.0 · 5547 in / 1231 out tokens · 38716 ms · 2026-05-12T01:14:20.833650+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We re-evaluated all conflicted samples through a human adjudication process involving 2 cross-cultural adjudicators... triple agreement increased by 6.38% for QAGS-C and 7.62% for SummEval.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages · 2 internal anchors

[1]

Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, P. Fung, Survey of hallucination in natural language generation, ACM Computing Surveys 55 (2023) 1–38. URL: http://dx.doi.org/10.1145/3571730

work page doi:10.1145/3571730 2023
[2]

Maynez, S

J. Maynez, S. Narayan, B. Bohnet, R. McDonald, On Faithfulness and Factuality in Abstractive Summarization, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Online, 2020, pp. 1906–

work page 2020
[3]

URL: https://aclanthology.org/2020.acl-main.173/

work page 2020
[4]

S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, Y. Cao, ReAct: Synergizing Reasoning and Acting in Language Models, in: International Conference on Learning Representations (ICLR), 2023. URL: https://arxiv.org/abs/2210.03629

work page internal anchor Pith review Pith/arXiv arXiv 2023
[5]

B. Plank, The "Problem" of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation, in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 2022, pp. 10671–10682. URL: https://aclanthology.org/2022.emnlp-main.731/

work page 2022
[6]

GPT-4 Technical Report

OpenAI, J. Achiam, S. Adler, S. Agarwal, et al., GPT-4 Technical Report, arXiv preprint arXiv:2303.08774 (2024). URL: https://arxiv.org/abs/2303.08774

work page internal anchor Pith review Pith/arXiv arXiv 2024
[7]

C. Niu, Y. Wu, J. Zhu, S. Xu, K. Shum, R. Zhong, J. Song, T. Zhang, RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models, in: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Bangkok, Thailand, 2024, pp. 108...

work page 2024
[8]

Kryscinski, B

W. Kryscinski, B. McCann, C. Xiong, R. Socher, Evaluating the Factual Consistency of Abstractive Text Summarization, in: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Online, 2020, pp. 9332–9346. URL: https://aclanthology.org/2020.emnlp-main.750/

work page 2020
[9]

A. Wang, K. Cho, M. Lewis, Asking and Answering Questions to Evaluate the Factual Consistency of Summaries, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Online, 2020, pp. 5008–

work page 2020
[10]

URL: https://aclanthology.org/2020.acl-main.450/

work page 2020
[11]

Durmus, H

E. Durmus, H. He, M. Diab, FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Online, 2020, pp. 5055–5070. URL: https://aclanthology.org/2020.acl-main.454/

work page 2020
[12]

Laban, T

P. Laban, T. Schnabel, P. N. Bennett, M. A. Hearst, SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization, Transactions of the Association for Computational Linguistics 10 (2022) 163–177. URL: https://aclanthology.org/2022.tacl-1.10/

work page 2022
[13]

Honovich, R

O. Honovich, R. Aharoni, J. Herzig, H. Taitelbaum, D. Kukliansy, V. Cohen, T. Scialom, I. Szpektor, A. Hassidim, Y. Matias, TRUE: Re-evaluating Factual Consistency Evaluation, in: Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering, Association for Computational Linguistics, Dublin, Ireland, 2022,...

work page 2022
[14]

Kovács, G

Á. Kovács, G. Recski, LettuceDetect: A Hallucination Detection Framework for RAG Applications, 2025. arXiv preprint arXiv:2502.17125 (2025) . URL: https://arxiv.org/abs/2502.17125

work page arXiv 2025
[15]

J. Song, X. Wang, J. Zhu, Y. Wu, X. Cheng, R. Zhong, C. Niu, RAG-HAT: A Hallucination- Aware Tuning Pipeline for LLM in Retrieval-Augmented Generation, in: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, Association for Computational Linguistics, Miami, Florida, US, 2024, pp. 1548–1558. URL: https://...

work page 2024
[16]

Gekhman, J

Z. Gekhman, J. Herzig, R. Aharoni, C. Elkind, I. Szpektor, TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models, in: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Singapore, 2023, pp. 2053–2070. URL: https://aclanthology.org/2023.emnlp-main.127

work page 2023
[17]

Nahum, N

O. Nahum, N. Calderon, O. Keller, I. Szpektor, R. Reichart, Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance, in: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025), Association for Computational Linguistics, Suzhou, China, 2025, pp. 26782–26809. URL: ht...

work page 2025
[18]

A. R. Fabbri, W. Kryściński, B. McCann, C. Xiong, R. Socher, D. Radev, SummEval: Re- evaluating Summarization Evaluation, Transactions of the Association for Computational Linguistics 9 (2021) 391–409. URL: https://aclanthology.org/2021.tacl-1.24/

work page 2021
[19]

Fernández-Pichel, M

M. Fernández-Pichel, M. Petrocchi, K. Roitero, M. Viviani, ROMCIR 2026: Overview of the 6th Workshop on Reducing Online Misinformation Through Credible Information Retrieval, European Conference on Information Retrieval (2026)

work page 2026