Update README.md

README.md

@@ -18,20 +18,18 @@ tools:
   - openai api
 ---
 # Rebuttal-RM 🏅 
-## Introduction 
+## 1. Introduction 
  **Rebuttal-RM** is a scoring model trained to automatically assess author responses in light of the target comment and its supporting context, with the explicit goal of matching human‐reviewer preferences.  The reward model, denoted **GRM**, receives as input the retrieved evidence chunks **CE**, the current review **R_i**, the target comment **target**, and a candidate reply **response**; it returns a vector of rubric-aligned scores *s* together with an explanatory rationale **e**.  Formally,
 
 $$
 (s,e)\;=\;\mathrm{GRM}\!\bigl(\,\sum_{p_j\in\text{CE}} p_j,\; R_i,\; c_{\text{target}},\; r_{\text{response}}\bigr).
 $$
 
-To obtain a robust evaluator we curate **102 K** training instances drawn from three sources: (i) 12 000 original author rebuttals that serve as a human baseline, (ii) GPT-4.1–refined answers representing an upper quality bound, and (iii) diverse model-generated replies (e.g., Qwen-2.5-3B, Claude-3.5) to broaden stylistic coverage.  Using **Qwen-3-8B** as the backbone, we fine-tune on this corpus to yield the final Rebuttal-RM.  As reported in Table 1, Rebuttal-RM achieves the strongest agreement with expert annotators, posting an average correlation of 0.812 and outperforming GPT-4.1 and DeepSeek-R1 by 9.0 % and 15.2 %, respectively.## 2 Performance (agreement with human ratings)
+To obtain a robust evaluator we curate **102 K** training instances drawn from three sources: (i) 12 000 original author rebuttals that serve as a human baseline, (ii) GPT-4.1–refined answers representing an upper quality bound, and (iii) diverse model-generated replies (e.g., Qwen-2.5-3B, Claude-3.5) to broaden stylistic coverage.  Using **Qwen-3-8B** as the backbone, we fine-tune on this corpus to yield the final Rebuttal-RM.  As reported in Table 1, Rebuttal-RM achieves the strongest agreement with expert annotators, posting an average correlation of 0.812 and outperforming GPT-4.1 and DeepSeek-R1 by 9.0 % and 15.2 %, respectively.
+
+## 2. Performance (agreement with human ratings)(higher = better)
 
-## 2 Performance (agreement with human ratings)
 
-### Table – Consistency between automatic scores and human ratings  
-*Higher values indicate better agreement.*
-### Consistency between automatic scores and human ratings (higher = better)
 | Scoring Model | Attitude r | Attitude β | Attitude f | Clarity r | Clarity β | Clarity f | Persuasiveness r | Persuasiveness β | Persuasiveness f | Constructiveness r | Constructiveness β | Constructiveness f | Avg |
 |-------------- |:---------:|:----------:|:----------:|:---------:|:----------:|:----------:|:-----------------:|:----------------:|:----------------:|:-------------------:|:-------------------:|:------------------:|:---:|
 | Qwen-3-8B      | 0.718 | 0.672 | 0.620 | 0.609 | 0.568 | 0.710 | 0.622 | 0.577 | 0.690 | 0.718 | 0.745 | 0.720 | 0.664 |
@@ -45,7 +43,7 @@ To obtain a robust evaluator we curate **102 K** training instances drawn from t
 | **Rebuttal-RM**| **0.839** | **0.828** | **0.910** | **0.753** | **0.677** | **0.790** | **0.821** | **0.801** | **0.820** | **0.839** | **0.835** | **0.810** | **0.812** |
 
 
-## 3 Deployment / Usage
+## 3. Deployment / Usage
 
 
 ### 3.1 Run with vLLM (OpenAI protocol)
@@ -120,7 +118,7 @@ Output format example:
               "Constructiveness": <int> }, 
   "score_explanation": <explanation for your given score>}"""
 ```
-## 4 Citation
+## 4. Citation
 ```
 @inproceedings{he2025rebuttalagent,
   title     = {RebuttalAgent: Strategic Persuasion in Academic Rebuttal via Theory of Mind},