Enhance dataset card: add paper/code links, task categories, description, and citation

README.md

@@ -3,35 +3,51 @@ configs:
 - config_name: default
   data_files:
   - split: law
-    path: "law.json"
+    path: law.json
   - split: psychology
-    path: "psychology.json"
+    path: psychology.json
   - split: chemistry
-    path: "chemistry.json"
+    path: chemistry.json
   - split: biology
-    path: "biology.json"
+    path: biology.json
   - split: physics
-    path: "physics.json"
+    path: physics.json
   - split: history
-    path: "history.json"
+    path: history.json
   - split: economics
-    path: "economics.json"
+    path: economics.json
   - split: math
-    path: "math.json"
+    path: math.json
   - split: business
-    path: "business.json"
+    path: business.json
   - split: philosophy
-    path: "philosophy.json"
+    path: philosophy.json
   - split: health
-    path: "health.json"
+    path: health.json
   - split: engineering
-    path: "engineering.json"
+    path: engineering.json
   - split: computer_science
-    path: "computer_science.json"
+    path: computer_science.json
   - split: other
-    path: "other.json"
+    path: other.json
+task_categories:
+- text-ranking
+language:
+- en
+tags:
+- reward-model
+- multi-domain
 ---
+
 # Reward of [test](https://huggingface.co/datasets/dongboklee/test) split extracted by dPRM-14B: dPRM-14B-test
+
+This dataset contains reward scores for multi-domain Chain-of-Thought (CoT) reasoning, specifically for the `test` split, extracted using the dPRM-14B reward model. It is part of the research presented in the paper [Rethinking Reward Models for Multi-Domain Test-Time Scaling](https://huggingface.co/papers/2510.00492).
+
+The paper investigates the reliability of large language models (LLMs) during test-time scaling using external verifiers or reward models to distinguish correct reasoning from flawed logic across 14 diverse domains. This particular dataset provides the outputs from one of the evaluated reward model variants.
+
+**Paper:** [Rethinking Reward Models for Multi-Domain Test-Time Scaling](https://huggingface.co/papers/2510.00492)
+**Code:** https://github.com/db-Lee/Multi-RM
+
 ## Usage
 ```python
 from datasets import load_dataset
@@ -42,3 +58,14 @@ dataset = load_dataset("dongboklee/dPRM-14B-test")
 # Load specific domain
 law_dataset = load_dataset("dongboklee/dPRM-14B-test", split="law")
 ```
+
+## Citation
+If you use this dataset in your research, please cite the associated paper:
+```bibtex
+@article{multi-rm,
+  title   = {Rethinking Reward Models for Multi-Domain Test-Time Scaling},
+  author  = {Lee, Dong Bok and Lee, Seanie and Park, Sangwoo and Kang, Minki and Baek, Jinheon and Kim, Dongki and Wagner, Dominik and Jin, Jiongdao and Lee, Heejun and Bocklet, Tobias and Wang, Jinyu and Fu, Jingjing and Hwang, Sung Ju and Bian, Jiang and Song, Lei},
+  journal = {arXiv preprint arXiv:2510.00492},
+  year    = {2025}
+}
+```