README.md

---
license: cc-by-4.0
task_categories:
- question-answering
- text-classification
language:
- en
tags:
- medical
- healthcare
- clinical-nlp
- benchmark
- counterfactual
- llm-evaluation
- maternal-health
- child-health
- robustness
pretty_name: MamaBench
size_categories:
- n<1K
---

# Dataset Card for MamaBench

## Dataset Description

- **Paper:** https://arxiv.org/abs/2607.14385
- **Organization:** HelpMum Africa

### Dataset Summary

MamaBench is a counterfactual clinical benchmark for evaluating the robustness of large language models on maternal and child health diagnostic reasoning. It consists of **217 counterfactual case pairs**, each pairing an original clinical vignette with a systematically perturbed counterfactual variant, designed to test whether a model's diagnostic reasoning is sensitive to clinically meaningful changes rather than relying on surface-level pattern matching.

MamaBench is released as a **single, unsplit evaluation set**. It is intended exclusively for **zero-shot evaluation** — see [Data Splits](#data-splits) below.

### Supported Tasks

- **Diagnostic robustness evaluation**: measuring whether a model's diagnosis changes appropriately (or fails to change) in response to clinically significant counterfactual perturbations.
- Benchmarking mitigation architectures (e.g., retrieval-augmented or evidence-anchored pipelines) against standalone model baselines.

### Languages

English.

## Dataset Structure

### Data Instances

Each pair consists of two rows sharing the same `pair_id` — one case presenting the original clinical scenario, and one presenting a counterfactually perturbed variant. The two narratives share overlapping surface features but differ in clinically decisive details, leading to different ground-truth diagnoses.

### Data Fields

- `case_id`: `string` — unique identifier for an individual case
- `pair_id`: `string` — shared identifier linking a case to its counterfactual partner; two `case_id`s share one `pair_id`
- `narrative`: `string` — first-person clinical presentation narrative
- `ground_truth`: `string` — the correct diagnosis for that specific narrative

Note: MamaBench does not store a static PP/PF/FP/FF label as a data field — that taxonomy is a derived evaluation output (whether a model gets the original and counterfactual case in a pair right or wrong), computed at scoring time, not part of the released schema.

### Data Splits

**MamaBench has no train/validation/test split.** All 217 pairs are released as a single evaluation set.

This is intentional: MamaBench is designed as a **held-out, zero-shot benchmark**. Using any portion of this dataset for fine-tuning, few-shot prompting, or in-context training will invalidate its use as an independent evaluation of model robustness. If you are developing or tuning a model, do not train on MamaBench.

## Dataset Creation

### Curation Rationale

Existing clinical LLM benchmarks largely evaluate static diagnostic accuracy and do not test whether a model's reasoning is robust to clinically meaningful changes in presentation — a gap that matters directly for safe deployment of LLM-based clinical decision support across healthcare settings generally, not any single region or resource context.

### Source Data

Case scenarios were authored by a team of clinician co-investigators, drawing on their clinical expertise. **No real patient records, PHI, or identifiable patient information were used at any stage.**

### Annotations

Each case's `ground_truth` diagnosis was assigned by the clinician co-investigators as part of authoring the narrative. The PP/PF/FP/FF taxonomy referenced elsewhere in this card is not an annotation on the data — it is computed at evaluation time from a model's outputs on a pair, not part of the dataset creation process.

### Personal and Sensitive Information

This dataset contains **no personal, identifiable, or patient-derived information**. All clinical scenarios are synthetic, authored by clinician co-investigators rather than drawn from real patient records.

## Considerations for Using the Data

### Intended Use

MamaBench is intended to support the evaluation and development of safer LLM-based clinical decision support systems for maternal and child health, across healthcare settings broadly.

### Limitations

- The benchmark is intentionally small (217 pairs) to allow high-quality expert curation over scale; it is not intended as a training corpus.
- Clinical scenarios are authored to reflect general clinical practice and guidelines and may not capture every regional or institutional variation in care.
- As with any fully public static benchmark, contamination risk increases over time as the data is scraped into future pretraining corpora (see note below).


## Additional Information

### Licensing Information

This dataset is released under the CC BY 4.0 license.

### Citation Information

```@misc{adewuyi2026mamabenchbenchmarkingllmrobustness,
      title={MamaBench: Benchmarking LLM Robustness in Maternal and Child Health Diagnosis through Counterfactual Clinical Perturbation}, 
      author={Thanni Adewuyi and Anuoluwa Sotome and Samuel Okoko and Angel Ezendu and Oluwafunke Akinbuwa and Oluwaseun Odunsi and Oluwasegun Oguntuase and Oluwadarasimi Oguntuase and Ifeoma Nwabueze and Abiodun Adereni},
      year={2026},
      eprint={2607.14385},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2607.14385}, 
}
```

### Ethics Statement

This study did not involve human subjects research. Case scenarios were authored by co-investigators drawing on clinical expertise; no patient data, identifiable information, or external human participants were involved.

### Contributions

Thanks to the clinician co-investigators who authored the benchmark scenarios, and to the HelpMum Africa AI/ML team.