Add paper link, project page, and task category

README.md

@@ -1,4 +1,11 @@
 ---
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- medical
+- triage
 dataset_info:
   features:
   - name: chosen
@@ -27,3 +34,32 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# PMR-Bench: Patient Message Ranking Benchmark
+
+[**Project Page**](https://tinyurl.com/Patient-Message-Triage) | [**Paper**](https://huggingface.co/papers/2601.13178)
+
+PMR-Bench is a large-scale public dataset for studying medical triage in the context of asynchronous outpatient portal messages. The benchmark treats patient message triage as a pairwise inference problem, where models are tasked to choose which of two messages is more medically urgent. 
+
+The dataset contains 1,569 unique messages and over 2,000 high-quality test pairs for pairwise medical urgency assessment. It includes unstructured patient-written messages paired with real electronic health record (EHR) data to emulate real-world medical triage scenarios.
+
+### Task Description
+The primary task is to perform a head-to-head re-sort of a physician's inbox by comparing pairs of messages. The dataset follows a "chosen" (more urgent) vs "rejected" (less urgent) format, making it suitable for training reward models (using Bradley-Terry objectives) or Supervised Fine-Tuning (SFT) for pairwise classification.
+
+### Data Fields
+- `chosen`: The patient message or EHR context determined to be more medically urgent.
+- `chosen_level`: Numerical urgency level for the chosen message.
+- `rejected`: The patient message or EHR context determined to be less medically urgent.
+- `rejected_level`: Numerical urgency level for the rejected message.
+- `difficulty`: A difficulty rating for the comparison.
+- `chosen_comments`/`rejected_comments`: Annotator feedback or reasoning.
+
+### Citation
+```bibtex
+@article{gatto2026medical,
+  title={Medical Triage as Pairwise Ranking: A Benchmark for Urgency in Patient Portal Messages},
+  author={Gatto, Joseph and Seegmiller, Parker and Burdick, Timothy and Resnik, Philip and Rahat, Roshnik and DeLozier, Sarah and Preum, Sarah M.},
+  journal={arXiv preprint arXiv:2601.13178},
+  year={2026}
+}
+```