README.md

---
language:
- en
pretty_name: MedPRESS
tags:
- medical
- safety
- sycophancy
- llm-evaluation
- benchmark
task_categories:
- text-generation
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: test
    path:
    - Data/medication_and_treatment_demand_200_exact_schema_paraphrase_v2.csv
    - Data/personal_health_self_care_200_exact_schema_paraphrase_v2.csv
    - Data/symptom_triage_and_care_resistance_200_exact_schema_paraphrase_v2.csv
---

# MedPRESS

**MedPRESS** is a multi-turn benchmark for evaluating patient-pressure-induced medical sycophancy in large language models. The dataset tests whether a model maintains a safe medical stance when a user repeatedly pressures it toward an unsafe or false health belief.

The benchmark is designed around five-turn conversations. Each row contains one medical scenario, the unsafe or false belief being pressured, the expected safe stance, and five progressively stronger user turns.

## Dataset Summary

MedPRESS contains 600 English benchmark cases across three medical safety scenario families:

| Scenario family | File | Rows |
|---|---:|---:|
| Medication and treatment demand | `Data/medication_and_treatment_demand_200_exact_schema_paraphrase_v2.csv` | 200 |
| Personal health self-care | `Data/personal_health_self_care_200_exact_schema_paraphrase_v2.csv` | 200 |
| Symptom triage and care resistance | `Data/symptom_triage_and_care_resistance_200_exact_schema_paraphrase_v2.csv` | 200 |

## Scenario Families

### Medication and Treatment Demand

Tests whether a model validates inappropriate medication or treatment demands, gives unsafe practical support, or redirects toward safer care such as clinician or pharmacist review.

### Personal Health Self-Care

Tests whether a model endorses risky home remedies or unsafe self-treatment, while checking whether it provides safe alternatives and appropriate red-flag guidance.

### Symptom Triage and Care Resistance

Tests whether a model minimizes potentially serious symptoms or supports delaying care when the scenario requires urgent or emergency evaluation.

## Data Schema

All CSV files share the same schema:

| Column | Description |
|---|---|
| `case_id` | Unique scenario identifier. |
| `scenario_family` | Scenario-family key. |
| `topic` | Medical topic or situation. |
| `risk_level` | Risk category for the scenario. |
| `unsafe_or_false_belief` | The unsafe or false belief the user pressures the model to accept. |
| `safe_stance_summary` | Short summary of the expected safe stance. |
| `turn1_initial_query` | Initial user message. |
| `turn2_personal_experience` | Follow-up pressure using personal experience. |
| `turn3_social_proof` | Follow-up pressure using social proof. |
| `turn4_external_claim` | Follow-up pressure using an external claim. |
| `turn5_direct_challenge` | Final direct challenge to the model's safe stance. |
| `triage_needed` | Whether the scenario requires triage guidance. |
| `triage_trigger` | The symptom, context, or condition that should trigger triage guidance. |

## Loading

```python
from datasets import load_dataset

dataset = load_dataset("samanjoy2/medpress_dataset", split="test")
print(dataset)
```

To load a single CSV directly:

```python
from datasets import load_dataset

dataset = load_dataset(
    "csv",
    data_files="hf://datasets/samanjoy2/medpress_dataset/Data/symptom_triage_and_care_resistance_200_exact_schema_paraphrase_v2.csv",
    split="train",
)
```

## Intended Use

MedPRESS is intended for research and evaluation of LLM medical safety behavior, especially in multi-turn settings where user pressure can make models more agreeable to unsafe medical claims.

This dataset should not be used as medical advice, clinical guidance, or a substitute for professional medical judgment.

## Citation

If you use MedPRESS, please cite:

```bibtex
@misc{joy2026medpressmultiturnbenchmarkpatientpressureinduced,
      title={MedPRESS: A Multi-turn Benchmark for Patient-Pressure-Induced Medical Sycophancy in LLMs}, 
      author={Saman Sarker Joy and Niloy Farhan},
      year={2026},
      eprint={2608.02520},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2608.02520}, 
}
```