Update README.md

README.md

@@ -448,3 +448,84 @@ configs:
   - split: full
     path: verifiability/full-*
 ---
+
+
+# RevUtil: Measuring the Utility of Peer Reviews for Authors
+
+[📄 Paper COMING SOON!]  
+[💻 GitHub Repository](https://github.com/bodasadallah/RevUtil)  
+
+---
+
+## 📚 Overview
+
+Providing **constructive feedback** to authors is a key goal of peer review. To support research on evaluating and generating useful peer review comments, we introduce **RevUtil**, a dataset for measuring the utility of peer review feedback.  
+
+RevUtil focuses on four main aspects of review comments:
+
+- **Actionability** – Can the author act on the comment?  
+- **Grounding & Specificity** – Is the comment concrete and tied to the paper?  
+- **Verifiability** – Can the statement be checked against the paper?  
+- **Helpfulness** – Does the comment assist the author in improving their work?  
+
+---
+
+## 🧑‍🔬 RevUtil Human
+
+- **1,430** review comments from real peer reviews.  
+- Each comment is annotated independently by **three human raters**.  
+- Labels are provided as `"gold"` (3/3 agreement), `"silver"` (2/3), or `"none"` (no agreement).  
+
+**Key columns:**
+
+| Column              | Description                                                                 |
+| ------------------- | --------------------------------------------------------------------------- |
+| `paper_id`          | ID of the reviewed paper                                                    |
+| `venue`             | Conference or journal name                                                  |
+| `focused_review`    | Full review (weakness + suggestion sections)                                |
+| `review_point`      | Individual review comment being evaluated                                   |
+| `id`                | Unique ID for the review point                                              |
+| `batch`             | Annotation batch/study identifier                                           |
+| `ASPECT`            | Dictionary with annotators and their labels                                 |
+| `ASPECT_label`      | Majority label (if available)                                               |
+| `ASPECT_label_type` | `"gold"`, `"silver"`, or `"none"`                                           |
+
+---
+
+## 🤖 RevUtil Synthetic
+
+- **10,000** synthetically labeled review comments generated with **GPT-4o**.  
+- Each example includes both a **score** and a **rationale**.  
+- Split into **9k train** / **1k test**.  
+
+**Key columns:**
+
+| Column                     | Description                                  |
+| -------------------------- | -------------------------------------------- |
+| `paper_id`                 | ID of the reviewed paper                     |
+| `venue`                    | Conference or journal name                   |
+| `focused_review`           | Full review (weakness + suggestion sections) |
+| `review_point`             | Individual review comment                    |
+| `id`                       | Unique ID for the review point               |
+| `chatgpt_ASPECT_score`     | Model-generated score for the aspect         |
+| `chatgpt_ASPECT_rationale` | Explanation of the score provided by GPT-4o  |
+
+---
+
+## 🚀 Usage
+
+You can load the datasets directly via 🤗 Datasets:
+
+```python
+from datasets import load_dataset
+
+# Human annotations
+human = load_dataset("boda/RevUtil_human")
+
+# Synthetic annotations
+synthetic = load_dataset("boda/RevUtil_synthetic")
+```
+
+
+
+## 📎 Citation