Himanshu167/AI-Response-Comparer

Model Description

This model is a fine-tuned version of microsoft/deberta-v3-base, optimized for Preference Classification (Reward Modeling). Instead of standard text classification, this model is designed to compare two AI-generated responses to the same prompt and predict which one is higher quality or more "preferred."

License

• Training scripts and source code are licensed under Apache-2.0.
• Model weights are released under CC BY-NC 4.0 due to dataset licensing restrictions.

Dataset

• Source: LLM Classification Finetuning (Kaggle)
• Context: The dataset consists of "Chatbot Arena" style prompts and paired completions, labeled by human preference.
• License: CC BY-NC 4.0 (Non-commercial use only).

Metrics

The model is evaluated using the following criteria, comparing the predicted probability distribution [P(A), P(B), P(Tie)] against the ground truth:

• Multi-class Log Loss (Primary):

  • Definition: Measures the distance between the predicted probability distribution and the actual labels. $$L=-\frac{1}{N}\sum _{i=1}^N\sum _{j=1}^M{y}_{i,j}\log (p_{i,j})L\; =\; -\backslash frac\{1\}\{N\}\; \backslash sum\_\{i=1\}^\{N\}\; \backslash sum\_\{j=1\}^\{M\}\; y\_\{i,j\}\; \backslash log(p\_\{i,j\})$$

  • Variables: Where $M=3M\; =\; 3$ (representing Response A, Response B, and Tie).

  • Why: It rewards the model for assigning higher probabilities to the correct outcome and heavily penalizes high-confidence incorrect predictions.

• Accuracy (Secondary):

  • Definition: The percentage of instances where the class with the highest predicted probability matches the ground truth label.
  • Calculation: Correct Predictions / Total Samples.

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Evaluation Results

The following results were achieved during final evaluation. Note that Accuracy was calculated using a local train/test split, while Log Loss follows the competition's evaluation framework.

Metric	Value	Source/Split
Multi-class Log Loss	1.0346	Kaggle Competition Metric
Accuracy	48.94%	Local Train/Test Split

Note on Performance:

• Log Loss: This score reflects the model's ability to provide well-calibrated probabilities for the three classes (A, B, and Tie) as required by the Kaggle competition.
• Accuracy: This was monitored locally to ensure the model was successfully learning the preference patterns beyond a random baseline (33.33%).

Acknowledgments & Attribution

• Base Model: This work utilizes DeBERTa-v3-base, developed by Microsoft.
• Dataset: Training data was provided by the LMSYS LLM Classification Finetuning competition on Kaggle.
• License Notice: This model is subject to the CC BY-NC 4.0 license due to the underlying dataset. It is intended for non-commercial, research, and educational purposes only.