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arxiv: 2605.28025 · v1 · pith:GLMLIBONnew · submitted 2026-05-27 · 💻 cs.AI · cs.CL· cs.CY

MIRA: A Bilingual Benchmark for Medical Information Response Audit

Mengyu Xu , Qiaoxin Yang , Qianqian Wang , Xiwei Dai , Weiyi Wu , Chongyang Gao This is my paper

Pith reviewed 2026-06-29 13:08 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.CY
keywords medical LLMshealth informationDifferential Information Dilutionbilingual benchmarkinformation audithealth literacyLLM safety evaluation
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The pith

LLMs consistently omit more medical information when users signal low health literacy.

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

The paper presents MIRA, a controlled bilingual benchmark built from 60 medically reviewed health questions and 4,320 prompt variations that test whether models deliver equivalent information across changes in language, register, and health-literacy signals. Across five mainstream LLMs, every model answered the questions, yet responses triggered by low health-literacy signals omitted more key facts, supplied fewer concrete next steps, and gave less support for independent judgment. The authors label this pattern Differential Information Dilution and show that language effects are model-specific rather than uniformly worse for non-English prompts. A knowledge-guided mitigation prompt reduced the dilution for most models.

Core claim

Large language models exhibit Differential Information Dilution on medical queries: when users employ low health-literacy signals, the models systematically omit more key information, provide fewer concrete next steps, and offer less support for independent judgment than they do for other phrasings of the same question, as demonstrated on the MIRA benchmark of 4,320 controlled prompts.

What carries the argument

The MIRA benchmark, which generates controlled prompt variations from 60 medically reviewed questions to isolate the effects of language, register, and health-literacy signals on response completeness.

If this is right

  • Safety evaluations of medical LLMs must test for information completeness across user phrasing signals rather than only for factual errors.
  • Knowledge-guided mitigation prompts can reduce information dilution for most models, with the largest measured reductions for Claude and Qwen.
  • Rank-order validity is supported by preliminary comparison with 300 real-world health queries.
  • Language effects on response quality are model-specific rather than a uniform non-English penalty.

Where Pith is reading between the lines

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

  • If the dilution pattern holds, users who already face health-literacy barriers may receive systematically less actionable information from the same models.
  • Developers could incorporate literacy-signal detection into prompt routing or response calibration to equalize information density.
  • Extending MIRA to higher-risk medical domains would test whether the dilution effect scales with clinical consequence.

Load-bearing premise

The 60 medically reviewed questions and the constructed prompt variations accurately isolate the effects of language, register, and health literacy signals without introducing other uncontrolled differences in how the models interpret the queries.

What would settle it

A new set of health questions or a different collection of models in which low health-literacy signals no longer produce measurably less complete responses than matched high-literacy signals.

Figures

Figures reproduced from arXiv: 2605.28025 by Chongyang Gao, Mengyu Xu, Qianqian Wang, Qiaoxin Yang, Weiyi Wu, Xiwei Dai.

Figure 1
Figure 1. Figure 1: DID across HLS: equivalent questions can [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: D3 underinformative simplification scores [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: DTI scores for D3 underinformative simplifi [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Mitigation effects on D3, Q2, and Q3 across [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: Rank-order alignment between MIRA syn￾thetic prompts and real-world medical help-seeking prompts. Each point represents one model-by-language group. Higher values indicate worse outcomes. 5.2 RQ2: Condition Effects on Information Dilution Both framing conditions yielded worse outcomes than the unframed baseline (Condition B): institu￾tional authority (Condition A) increased D3 and Q2, and user-assumes-risk… view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of the 60 MIRA seed questions [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
read the original abstract

Large language models (LLMs) are increasingly used to provide public-facing health information, yet existing safety evaluations overlook whether responses preserve comparable medical information across different user phrasings of the same question. To address this, we introduce the Medical Information Response Audit (MIRA), a bilingual, controlled benchmark that assesses whether LLMs provide comparable medical information across user-side language, register, and health literacy signals. MIRA contains 4,320 prompts built from 60 medically reviewed, low-risk health questions. Across five mainstream LLMs, models answered all medical questions, but responses to low health-literacy signals consistently omitted more key information, provided fewer concrete next steps, and offered less support for independent judgment. We term this pattern Differential Information Dilution (DID). Language effects are model-specific rather than uniformly worse for non-English prompts. A comparison with 300 real-world health queries provides preliminary evidence of rank-order validity. A knowledge-guided mitigation prompt reduces information dilution for most models, with the largest reductions in underinformative simplification observed for Claude (~8%) and Qwen (~6%).

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

3 major / 2 minor

Summary. The paper introduces the Medical Information Response Audit (MIRA), a bilingual benchmark with 4,320 prompts constructed from 60 medically reviewed low-risk health questions. It evaluates five mainstream LLMs for whether responses preserve comparable medical information across variations in user language, register, and health literacy signals. The central empirical finding is a consistent pattern termed Differential Information Dilution (DID), in which low health-literacy signals produce responses that omit more key information, supply fewer concrete next steps, and offer less support for independent judgment; language effects are reported as model-specific rather than uniformly worse for non-English prompts. A preliminary comparison against 300 real-world health queries is offered for rank-order validity, and a knowledge-guided mitigation prompt is shown to reduce dilution for most models (largest effects for Claude and Qwen).

Significance. If the DID pattern proves robust after controlling for prompt confounds, the work identifies a previously under-examined equity risk in public-facing health LLMs and supplies a reusable benchmark that could guide mitigation research. The bilingual scope and the empirical demonstration that all models answered the questions yet varied systematically in information completeness are useful contributions; the mitigation result supplies an initial, falsifiable intervention point.

major comments (3)
  1. [Abstract / §3] Abstract and §3 (Benchmark Construction): the claim that MIRA is a 'controlled benchmark' isolating language, register, and health-literacy signals is load-bearing for the DID attribution, yet the text provides no evidence of length/token-count matching, syntactic-complexity controls, or ablation tests on the 4,320 prompt variants; low-literacy variants are described as 'built from' the base questions without quantifying or ruling out these mechanical differences.
  2. [§4] §4 (Evaluation and Scoring): the medical review of the 60 questions and the subsequent response scoring report no inter-rater reliability statistics, no error bars on omission counts, and no statistical tests comparing conditions; without these, the quantitative basis for 'consistently omitted more key information' cannot be assessed for reproducibility or effect size.
  3. [§5.2] §5.2 (Real-world Validation): the comparison with 300 real-world queries is labeled 'preliminary' and supplies no sampling protocol, matching criteria to the benchmark questions, or statistical test of rank-order agreement; this section is therefore insufficient to support the validity claim.
minor comments (2)
  1. [Table 1] Table 1 (or equivalent prompt-count table) should explicitly list mean token lengths and variance for each literacy/register/language cell so readers can evaluate the confound risk directly.
  2. [§2] The term 'Differential Information Dilution (DID)' is introduced without a formal operational definition or scoring rubric; a short appendix defining the three components (omissions, next steps, support for judgment) would improve reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which help clarify the presentation of our benchmark and findings. We address each major comment below and commit to revisions that strengthen the quantitative and methodological rigor of the manuscript.

read point-by-point responses

  1. Referee: [Abstract / §3] Abstract and §3 (Benchmark Construction): the claim that MIRA is a 'controlled benchmark' isolating language, register, and health-literacy signals is load-bearing for the DID attribution, yet the text provides no evidence of length/token-count matching, syntactic-complexity controls, or ablation tests on the 4,320 prompt variants; low-literacy variants are described as 'built from' the base questions without quantifying or ruling out these mechanical differences.

    Authors: We agree that explicit controls are needed to support the 'controlled benchmark' framing. Section 3 describes systematic variation of language, register, and literacy signals around fixed medical content, but does not report token counts, syntactic metrics, or ablations. In revision we will add: (i) average token lengths and Flesch-Kincaid scores per condition, (ii) a table confirming length matching within 10% across variants, and (iii) an ablation on a 20% subset of prompts that isolates mechanical features. These additions will be placed in §3 and the appendix. revision: yes

  2. Referee: [§4] §4 (Evaluation and Scoring): the medical review of the 60 questions and the subsequent response scoring report no inter-rater reliability statistics, no error bars on omission counts, and no statistical tests comparing conditions; without these, the quantitative basis for 'consistently omitted more key information' cannot be assessed for reproducibility or effect size.

    Authors: We acknowledge the absence of these statistics. The 60 questions were reviewed by two board-certified physicians with consensus resolution; response scoring was performed by the same reviewers using a structured rubric. In the revision we will report: Cohen’s kappa for inter-rater agreement on both question review and omission scoring, standard-error bars on all omission and next-step counts, and paired statistical tests (Wilcoxon signed-rank or mixed-effects models) comparing high- vs. low-literacy conditions. These will appear in §4 and a new supplementary table. revision: yes

  3. Referee: [§5.2] §5.2 (Real-world Validation): the comparison with 300 real-world queries is labeled 'preliminary' and supplies no sampling protocol, matching criteria to the benchmark questions, or statistical test of rank-order agreement; this section is therefore insufficient to support the validity claim.

    Authors: The section is intentionally labeled preliminary. We will expand it to include: the exact sampling protocol (public health-forum posts collected under IRB-approved de-identification), topic-matching criteria (manual alignment to the 60 MIRA questions by two annotators), and a Spearman rank-correlation test between model rankings on the benchmark and real-world set. We will also add an explicit limitations paragraph. These changes keep the section’s preliminary character while addressing the referee’s concerns. revision: yes

Circularity Check

0 steps flagged

No circularity: direct empirical counts from benchmark runs

full rationale

The paper constructs MIRA as 4,320 prompts from 60 base questions, runs five LLMs, and reports direct counts of omitted information, next steps, and judgment support. The DID pattern is defined as the observed difference in those counts across literacy signals; no equations, fitted parameters, or predictions are introduced that could reduce to the inputs by construction. External comparison to 300 real-world queries is cited only for rank-order validity, not as load-bearing justification. No self-citation chains, uniqueness theorems, or ansatzes appear. The work is therefore self-contained empirical measurement.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Abstract-only review limits visibility; the central observations rest on the assumption that expert medical review of the 60 questions is sufficient and that prompt variations cleanly isolate the targeted signals.

axioms (1)
  • domain assumption Low-risk health questions exist that can be reliably identified and medically reviewed for accuracy by experts.
    Benchmark is built from 60 such questions.
invented entities (1)
  • Differential Information Dilution (DID) no independent evidence
    purpose: Label for the observed pattern of reduced information in responses to low health-literacy prompts.
    Term coined in the abstract to describe the empirical finding; no independent evidence outside the benchmark.

pith-pipeline@v0.9.1-grok · 5733 in / 1361 out tokens · 24557 ms · 2026-06-29T13:08:01.259058+00:00 · methodology

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Reference graph

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8 extracted references · 2 canonical work pages · 1 internal anchor

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