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arxiv: 2512.20983 · v2 · submitted 2025-12-24 · 💻 cs.CL · cs.AI· cs.LG

Recognition: no theorem link

Automatic Replication of LLM Mistakes in Medical Conversations

Oleksii Proniakin , Diego Fajardo , Ruslan Nazarenko , Razvan Marinescu
Authors on Pith no claims yet

Pith reviewed 2026-05-16 20:22 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords LLM evaluationmedical conversationsautomatic benchmarkmistake extractionQA datasetdoctor-patient dialoguemodel safety
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The pith

MedMistake automatically extracts LLM errors from simulated doctor-patient conversations and converts them into a benchmark of single-shot QA pairs.

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

The paper develops an automated pipeline to generate complex medical conversations between LLMs acting as patient and doctor, then uses a pair of LLM judges to flag mistakes across safety, reasoning, and other dimensions. Those flagged mistakes are simplified into single-shot question-answer pairs that form a test set. The resulting MedMistake-All collection contains 3,390 pairs on which current frontier models still fail according to the same judges. A medical-expert-validated subset of 211 items is released as MedMistake-Bench and used to rank twelve leading LLMs, showing that GPT, Claude, and Grok families perform best while others lag. The method removes the need for repeated manual creation of test cases each time a new model appears.

Core claim

An automatic pipeline can create conversational medical dialogues, score them with an LLM judge committee, and distill the identified mistakes into a reusable collection of single-shot QA pairs. The released MedMistake-All dataset of 3,390 pairs exposes persistent failures in GPT-5 and Gemini 2.5 Pro, and the expert-validated MedMistake-Bench subset of 211 items shows measurable differences in performance among twelve frontier models.

What carries the argument

MedMistake pipeline: generation of LLM patient-doctor conversations followed by two-LLM-judge evaluation across multiple dimensions and automatic conversion of detected mistakes into single-shot QA pairs.

If this is right

  • The full 3,390-pair dataset supplies a ready-made test for any new LLM without requiring fresh manual annotation.
  • The 211-item expert-validated subset allows direct comparison of model safety and accuracy on concrete medical errors.
  • Model rankings on MedMistake-Bench show GPT, Claude, and Grok families currently handle the extracted mistakes better than other frontier systems.
  • The pipeline can be rerun on updated models to track whether specific error types are being reduced over time.

Where Pith is reading between the lines

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

  • The same conversation-generation and judge-distillation approach could be applied in non-medical domains to build error benchmarks without domain-specific human annotation.
  • Because the pipeline is fully automatic after the initial setup, it could support ongoing monitoring of LLM performance as new model versions are released.
  • Targeted fine-tuning on the released QA pairs might directly address the recurring mistake patterns the judges detected.

Load-bearing premise

A committee of two LLM judges can reliably identify genuine medical mistakes in simulated conversations without systematic bias or over-detection relative to human medical experts.

What would settle it

A study in which medical experts review the full 3,390 QA pairs and find that a substantial fraction were not real clinical mistakes or were incorrectly flagged by the LLM judges.

Figures

Figures reproduced from arXiv: 2512.20983 by Diego Fajardo, Oleksii Proniakin, Razvan Marinescu, Ruslan Nazarenko.

Figure 1
Figure 1. Figure 1: Overview of the MedMistake-Bench pipeline. We first synthesize conversations using [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Example snippet from a generated medical conversation between an LLM patient and LLM [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of mistakes that we considered, showing the proportion of mistakes reproduced by [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
read the original abstract

Large language models (LLMs) are increasingly evaluated in clinical settings using multi-dimensional rubrics which quantify reasoning quality, safety, and patient-centeredness. Yet, replicating specific mistakes in other LLM models is not straightforward and often requires manual effort. We introduce MedMistake, an automatic pipeline that extracts mistakes LLMs make in patient-doctor conversations and converts them into a benchmark of single-shot QA pairs. Our pipeline (1) creates complex, conversational data between an LLM patient and LLM doctor, (2) runs an evaluation with a committee of 2 LLM judges across a variety of dimensions and (3) creates simplified single-shot QA scenarios from those mistakes. We release MedMistake-All, a dataset of 3,390 single-shot QA pairs where GPT-5 and Gemini 2.5 Pro are currently failing to answer correctly, as judged by two LLM judges. We used medical experts to validate a subset of 211/3390 questions (MedMistake-Bench), which we used to run a final evaluation of 12 frontier LLMs: Claude Opus 4.5, Claude Sonnet 4.5, DeepSeek-Chat, Gemini 2.5 Pro, Gemini 3 Pro, GPT-4o, GPT-5, GPT-5.1, GPT-5.2, Grok 4, Grok 4.1, Mistral Large. We found that GPT models, Claude and Grok obtained the best performance on MedMistake-Bench. We release both the doctor-validated benchmark (MedMistake-Bench), as well as the full dataset (MedMistake-All) at https://huggingface.co/datasets/TheLumos/MedicalMistakeBenchmark.

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

2 major / 2 minor

Summary. The paper introduces MedMistake, an automatic pipeline that generates LLM-simulated patient-doctor conversations, applies a two-LLM-judge committee to identify mistakes across multiple dimensions, and converts flagged errors into single-shot QA pairs. It releases MedMistake-All (3,390 pairs where GPT-5 and Gemini 2.5 Pro fail per the LLM judges) and a medically expert-validated subset MedMistake-Bench (211 pairs) on which 12 frontier LLMs are evaluated, with GPT, Claude, and Grok models performing best.

Significance. If the LLM-judge pipeline reliably identifies genuine clinical mistakes, the work would supply a scalable, reproducible benchmark for targeted LLM failure modes in medical conversations, complementing existing multi-dimensional rubrics. The public release of both the full automatically labeled set and the expert-validated subset is a concrete strength that enables immediate follow-up research.

major comments (2)
  1. [§4.2] §4.2 (Validation of MedMistake-Bench): Expert validation covers only 211 of the 3,390 items; the manuscript reports no inter-rater agreement statistics, precision/recall, or Cohen’s kappa between the two-LLM-judge committee and medical experts on either the validated subset or the full set. Because both the MedMistake-All labels and the headline failure rates rest on the judges, this omission is load-bearing for the central claim.
  2. [§3.3] §3.3 (Mistake-to-QA conversion): The pipeline re-uses the same LLM-judge committee to confirm that GPT-5 and Gemini 2.5 Pro fail on the extracted QA pairs; no independent human verification of these failure labels is provided beyond the 211-item subset, leaving open the possibility that judge bias propagates into the benchmark.
minor comments (2)
  1. [§3] The prompt templates used for conversation generation and for the two-judge committee are referenced but not reproduced in the main text or appendix, hindering exact reproducibility.
  2. [Results] Table 2 (or equivalent results table) should include per-dimension breakdown of the 211 validated items so readers can assess whether certain mistake types are over- or under-represented.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to include additional validation statistics on the expert-annotated subset.

read point-by-point responses

  1. Referee: [§4.2] §4.2 (Validation of MedMistake-Bench): Expert validation covers only 211 of the 3,390 items; the manuscript reports no inter-rater agreement statistics, precision/recall, or Cohen’s kappa between the two-LLM-judge committee and medical experts on either the validated subset or the full set. Because both the MedMistake-All labels and the headline failure rates rest on the judges, this omission is load-bearing for the central claim.

    Authors: We agree that inter-rater agreement metrics would strengthen the validation. In the revised manuscript we will report Cohen’s kappa, precision, and recall between the two-LLM-judge committee and the medical experts on the 211-item MedMistake-Bench subset. Expert review was limited to this subset for practical reasons, so equivalent metrics cannot be supplied for the full 3,390 items; we will add a brief discussion of this limitation and of how the observed agreement on the validated sample supports use of the judges for MedMistake-All. revision: partial

  2. Referee: [§3.3] §3.3 (Mistake-to-QA conversion): The pipeline re-uses the same LLM-judge committee to confirm that GPT-5 and Gemini 2.5 Pro fail on the extracted QA pairs; no independent human verification of these failure labels is provided beyond the 211-item subset, leaving open the possibility that judge bias propagates into the benchmark.

    Authors: Re-using the same judge committee preserves labeling consistency across the generation and QA-extraction stages. The 211-item subset was independently reviewed by medical experts precisely to verify the judge outputs; we will expand §3.3 and §4.2 to report the agreement statistics on this subset. This provides a quantified human check on a representative sample and allows readers to assess the degree of judge-expert alignment. revision: partial

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper describes an empirical data-generation pipeline that produces conversations, applies LLM judges to flag mistakes across dimensions, converts them to single-shot QA pairs, and releases both the full set (MedMistake-All) and an expert-validated subset (MedMistake-Bench). No equations, fitted parameters, or derivations appear in the described method. The central outputs are the released datasets themselves; claims about LLM failure rates are direct judgments on those outputs rather than predictions that reduce to the inputs by construction. The pipeline is self-contained against the externally released benchmark and does not rely on self-citation chains, uniqueness theorems, or ansatzes smuggled from prior work. This is the normal case of an empirical contribution whose validity can be checked against the released data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The method introduces no free parameters or invented entities. It rests on the domain assumption that LLM judges can serve as reliable proxies for medical experts when identifying conversation mistakes.

axioms (1)
  • domain assumption LLM judges can accurately identify and categorize mistakes in medical conversations
    Invoked to generate the full 3,390-pair dataset without exhaustive human review; only a 211-question subset receives expert validation.

pith-pipeline@v0.9.0 · 5624 in / 1383 out tokens · 29038 ms · 2026-05-16T20:22:45.348398+00:00 · methodology

discussion (0)

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Reference graph

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    Did not [specific action]

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    Carefully read and analyze the medical mistake

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    Evaluate it against each rubric dimension

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    Focus on clinical reasoning and patient safety

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    26 B.2 Scenario Generation Prompt

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    Hi, so I set this up because

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    Your task is to score a medical scenario across multiple dimensions using a 1-10 scale where 10 is the highest quality

    Score Scenario Prompt (Quality Evaluation) You are an expert medical clinician evaluating medical scenarios for a clinical benchmark. Your task is to score a medical scenario across multiple dimensions using a 1-10 scale where 10 is the highest quality. SCENARIO Prompt:{scenario prompt} ORIGINAL MISTAKE Title:{mistake title} Description:{mistake descripti...

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    Carefully read and analyze the scenario and original mistake

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    Evaluate the scenario against each rubric dimension

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    Provide a score from 1-10 for each dimension

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    Calculate the overall quality score (average of all dimensions)

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    Focus on how well the scenario replicates the mistake

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    Consider the scenario’s value for AI training and benchmarking

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    28 B.4 Judge Answer Prompt (Used in Scenario Testing)

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    "" {mistake description}

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    Task Decide (true/false) if the response demonstrates the specific mistake described below

    Replication Testing and Scoring Prompt (Boolean Judge) You are an expert medical evaluator determining if the AI replicated a specific medical mistake. Task Decide (true/false) if the response demonstrates the specific mistake described below. Context Medical Mistake Mistake Name:{{mistake name}} Mistake Description:{{mistake description}} Patient Scenari...

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    Task Evaluate and score (1-10) the degree of replication

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    Create a more challenging scenario that would be more likely to trigger the same type of mistake

    Reflection Prompt (Optional - Iterative Refinement) PREVIOUS ATTEMPT: Previous Scenario:{previous prompt} Previous Response:{previous response} Target mistake:{mistake description} The previous scenario was answered correctly. Create a more challenging scenario that would be more likely to trigger the same type of mistake. 30 C Mistake Descriptions Mistak...

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