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arxiv: 2604.05593 · v1 · submitted 2026-04-07 · 💻 cs.AI · cs.CL

Recognition: no theorem link

Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge

Xin Sun , Di Wu , Sijing Qin , Isao Echizen , Abdallah El Ali , Saku Sugawara
Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:34 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords label biasLLM-as-a-Judgetrust assessmentheuristic cuesource disclosureAI evaluationhuman-AI comparisonattention patterns
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The pith

Both humans and LLMs assign higher trust to identical information when labeled human-authored than when labeled AI-generated.

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

The paper tests whether disclosed source labels shape trust judgments in evaluation settings. It presents the same content under swapped labels and measures responses from people and from LLMs used as judges. Both groups show higher trust for the human label. Eye-tracking and internal model analysis reveal that labels function as quick heuristic cues, with attention and uncertainty patterns aligning between humans and models. The finding questions the fairness of label-exposed LLM evaluations and suggests that aligning models to human judgments may carry over the same shortcut reliance.

Core claim

Using a counterfactual setup that holds all text constant and varies only the source label, the work shows that trust ratings rise when content is marked as human-authored and fall when marked as AI-generated. This pattern appears in both human participants and LLM judges. Model attention concentrates more on the label region than the content region, with stronger label focus under human labels, while decision logits indicate greater uncertainty under AI labels. These internal patterns match the human eye-tracking data, indicating that the source label acts as a shared heuristic cue.

What carries the argument

The counterfactual design that isolates the source label by presenting identical content under human versus AI authorship disclosures.

If this is right

  • LLM-as-a-Judge systems may systematically undervalue AI-generated outputs when source labels are visible.
  • Alignment procedures that train on human preferences risk embedding label-based heuristics into model behavior.
  • Evaluation validity suffers when labels are disclosed, because judgments track the label more than the content.
  • Attention and uncertainty metrics in LLMs can serve as detectable signals of this heuristic reliance.

Where Pith is reading between the lines

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

  • The same label effect could appear in other judgment tasks such as quality scoring or fact-checking beyond trust.
  • Blinding source labels during both human and model evaluation might eliminate the bias and produce more content-focused assessments.
  • If training data contain labeled examples, models may learn to overweight labels even when labels are not explicitly provided at inference time.

Load-bearing premise

The experiment successfully keeps every factor except the source label identical across conditions, so no other cue influences the trust difference.

What would settle it

A replication in which the source label is removed or replaced with a neutral marker and the trust gap between the former human and AI conditions disappears for both humans and LLMs.

Figures

Figures reproduced from arXiv: 2604.05593 by Abdallah El Ali, Di Wu, Isao Echizen, Saku Sugawara, Sijing Qin, Xin Sun.

Figure 1
Figure 1. Figure 1: Three studies examine how source labels affect trust judgments by an LLM-as-a-judge and investigate the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An example heatmap of gaze points on stimuli that are displayed on the lab monitor during the human [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (Left): LLM’s attention allocation between two AoAs (i.e., label vs. content) and (Right): LLM’s logit entropy, across two label conditions (i.e., Human vs. AI). (**p<.01, *p<.05). Mean of Fixation Count Mean of Fixation Duration 8 - 7 - 6 - 5 - 4 - 3 - 2 - 1 - 0 - AOI-Label AOI-Label AOI-Content AOI-Content [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Analyses by GEE test (Hardin and Hilbe, 2012) with FDR correction (Haynes, 2013) of human gaze patterns (i.e., fixation count and fixation duration) in two AoIs. (**p<.01, *p<.05, “ns” is not significant). allocation and decision confidence. Across mod￾els, attention is consistently label-dominant, with stronger label-AoA attention under Human than AI labels, while decision uncertainty is highest under AI … view at source ↗
Figure 7
Figure 7. Figure 7: Instruction shown to the participants during [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Human trust ratings in Study 1. Top: Trust score distributions for the four (2 × 2) conditions cross￾ing true answer source (Human vs. LLM) and disclosed label (Human vs. AI). Bottom: Main effects collapsed across the other factor: trust scores by true source (re￾gardless of label) and by disclosed label (regardless of true source). Violins show density; red dots indicate means; black lines indicate median… view at source ↗
Figure 9
Figure 9. Figure 9: Trust scores of LLM-as-a-Judge under three label conditions from Study 3 (Sec. 3.3). Mean trust ratings (with error bars) produced by each model when the same health QA content is shown across three labels: Human, AI, and a non-semantical Placebo label as “[TAG]”. Horizontal brackets indicate significant pair￾wise differences (∗∗∗p < .001, ∗∗p < .01, ∗p < .05). Attention Density (LogRatio) Logit Entropy (E… view at source ↗
Figure 10
Figure 10. Figure 10: reports the attention allocation “LogRa￾tio” between Label AoA and Content AoA across three label conditions. Across all models and condi￾tions, LogRatio is consistently above zero, indicat￾ing consistent label-dominant attention allocation at the judgment step. The placebo condition often elicits the largest LogRatio, suggesting that an un￾derspecified label can attract extra processing to the label regi… view at source ↗
Figure 11
Figure 11. Figure 11: shows the logits entropy at the judgment step under three label conditions. Across models, the AI label generally yields higher entropy than Trust Scores [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: LLM attention distribution density across [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
read the original abstract

Large language models (LLMs) are increasingly used as automated evaluators (LLM-as-a-Judge). This work challenges its reliability by showing that trust judgments by LLMs are biased by disclosed source labels. Using a counterfactual design, we find that both humans and LLM judges assign higher trust to information labeled as human-authored than to the same content labeled as AI-generated. Eye-tracking data reveal that humans rely heavily on source labels as heuristic cues for judgments. We analyze LLM internal states during judgment. Across label conditions, models allocate denser attention to the label region than the content region, and this label dominance is stronger under Human labels than AI labels, consistent with the human gaze patterns. Besides, decision uncertainty measured by logits is higher under AI labels than Human labels. These results indicate that the source label is a salient heuristic cue for both humans and LLMs. It raises validity concerns for label-sensitive LLM-as-a-Judge evaluation, and we cautiously raise that aligning models with human preferences may propagate human heuristic reliance into models, motivating debiased evaluation and alignment.

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.

Referee Report

0 major / 2 minor

Summary. The paper claims that source labels (human-authored vs. AI-generated) bias trust judgments in both humans and LLMs used as judges. Using a counterfactual design that holds content constant while manipulating only the label, the authors report higher trust ratings for human-labeled content. Human data include explicit ratings and eye-tracking fixation metrics showing heavy label reliance; LLM data include judgment outputs, denser attention weights on label tokens (stronger for human labels), and higher logit-based uncertainty for AI labels. These patterns are interpreted as evidence of shared heuristic reliance on source labels, raising concerns for the validity of LLM-as-a-Judge evaluations and potential propagation of biases via alignment.

Significance. If the central empirical result holds under the reported controls, the work is significant for AI evaluation research because it identifies a concrete, measurable bias that affects both human and model judgments in the same direction. The convergence between behavioral (eye-tracking) and internal-state (attention and uncertainty) measures provides a rare cross-species link between cognitive heuristics and model mechanisms, directly supporting the call for debiased evaluation protocols.

minor comments (2)
  1. Abstract: the summary of results would be strengthened by including at least the total sample sizes for human participants and LLM trials, along with the primary statistical test outcomes or effect sizes that support the trust difference claim.
  2. The description of LLM attention analysis should clarify how label-region attention weights are normalized and aggregated across layers to ensure direct comparability with the human eye-tracking fixation metrics.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of our work, the accurate summary of our findings, and the recommendation for minor revision. We are pleased that the cross-species convergence between human behavioral measures and LLM internal states was recognized as significant for AI evaluation research.

Circularity Check

0 steps flagged

Empirical study with no derivational chain or self-referential reductions

full rationale

This paper reports an empirical observational study using counterfactual designs to compare trust judgments by humans and LLMs under manipulated source labels. No mathematical derivations, equations, fitted parameters, or predictions are present that could reduce to inputs by construction. Claims rest on experimental measurements (ratings, eye-tracking, attention weights, logits) with reported controls for confounds, and no load-bearing self-citations or uniqueness theorems are invoked to justify core results. The analysis is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of the counterfactual experimental design and the interpretation of eye-tracking and model internals as direct evidence of heuristic reliance. No free parameters, new entities, or non-standard axioms are mentioned.

axioms (1)
  • standard math Standard assumptions of experimental psychology and statistical inference hold for trust ratings, gaze data, and model logits.
    Invoked implicitly to interpret differences across label conditions as evidence of heuristic reliance.

pith-pipeline@v0.9.0 · 5500 in / 1253 out tokens · 69878 ms · 2026-05-10T19:34:29.741413+00:00 · methodology

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    Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. https://arxiv.org/abs/2306.05685 Judging llm-as-a-judge with mt-bench and chatbot arena . Preprint, arXiv:2306.05685

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    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 INTEGERS output.state before.all mid.sentence after.sentence after.block STRING...

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    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...