README.md

metadata

license: cc-by-4.0
language:
  - en
  - ja
  - zu
  - yo
  - zh
  - ko
  - th
  - sw
tags:
  - medical
size_categories:
  - 1K<n<10K

MultiMed-X

MultiMed-X is a multilingual benchmark for medical reasoning evaluation across natural language inference (NLI) and open-ended question answering (QA).
The dataset is designed to assess reasoning quality, factual accuracy, and localization of large language models in non-English medical settings, with particular emphasis on low-resource languages.

This dataset accompanies the paper: MED-COREASONER: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning.

---

Dataset Overview

MultiMed-X-350 is constructed by translating and expert-validating two established English medical benchmarks:

• BioNLI → Multilingual medical natural language inference (NLI), original data from BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic Constraints for Adversarial Examples.
• LiveQA → Multilingual open-ended medical question answering (QA), original data from LiveQA: A Question Answering Dataset over Sports Live.

Each instance is translated into multiple target languages and independently reviewed and revised by bilingual medical experts to ensure clinical correctness and linguistic naturalness.

Languages

The dataset covers 7 non-English languages:

• Chinese (ZH)
• Japanese (JA)
• Korean (KO)
• Swahili (SW)
• Thai (TH)
• Yoruba (YO)
• Zulu (ZU)

---

Data Format

All data are released as a single unified table (e.g., JSONL / Parquet compatible with Hugging Face datasets).

Common Fields

Field	Type	Description
id	string	Unique instance ID
lang	string	Language code (e.g., zu, sw)
task	string	Task type: nli or qa
source	string	Data source (BioNLI or LiveQA)
text	string	Original content in the target language
label	string / null	Gold label (NLI only)

---

ID Convention

• NLI (BioNLI)
  bionli-<lang>-XYZ

• QA (LiveQA)
  qa-<lang>-XYZ

Only 3-digit numeric suffixes are used.

---

Example Entries

NLI Example

{
  "id": "bionli-zu-042",
  "lang": "zu",
  "task": "nli",
  "source": "BioNLI",
  "text": "Premise: ... Hypothesis: ...",
  "label": "entailment"
}

QA Example

{
  "id": "qa-sw-117",
  "lang": "sw",
  "task": "qa",
  "source": "LiveQA",
  "text": "Swali: ... Jibu: ...",
  "label": null
}

---

Data Statistics

• 350 instances per language
  • 150 NLI (BioNLI)
  • 200 QA (LiveQA)
• ~2,450 total instances
• Annotated and validated by ~12 physicians or senior medical students

---

Intended Use

MultiMed-X-350 is intended for:

• Multilingual medical reasoning evaluation
• Cross-lingual robustness analysis
• Low-resource language benchmarking
• Evaluation of reasoning strategies (e.g., CoT, structured reasoning, agentic systems)

⚠️ Not intended for clinical deployment or direct medical decision-making.

---

Ethical Considerations

• All data are derived from publicly available datasets
• Translations are expert-reviewed
• No private patient data are included
• Annotators were formally recruited and compensated or credited as co-authors

---

Citation

@article{gao2026medcoreasoner,
  title={MED-COREASONER: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning},
  author={Gao, Fan and Tong, Sherry T. and Sohn, Jiwoong and Huang, Jiahao and Jiang, Junfeng and Xia, Ding and Ittichaiwong, Piyalitt and Veerakanjana, Kanyakorn and Kim, Hyunjae and Chen, Qingyu and Taylor, Edison Marrese and Kobayashi, Kazuma and Aizawa, Akiko and Li, Irene},
  journal={arXiv preprint arXiv:2601.08267},
  year={2026}
}

---

License

This dataset is released for research and evaluation purposes only, under the same licensing terms as the original source datasets (BioNLI, LiveQA).