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Metrics Ensemble

1. Automatic Ensemble Creation: Implement functionality for automatic creation of evaluation ensembles

• Train your own RAG evaluation model (i. e. Gradient Boosting Classifier) based on the RuRAGE metrics
• Prepare data for the RuRAGE ensemble model training right from the RuRAGE reports
• Train your own RuRAGE ensemble model for any of the evaluation tasks: Correctness, Faithfulness, Relevance
• Save (only basic functionality is implemented) and use trained model on the inference

2. Auto-adaptive thresholds: Implement functionality for automatic creation thresholds for features in ensemble

• Automatic ensemble model optimization by selecting an optimal classification threshold(s)

3. Multiclass Labels: Extend support to work with multiclass labels.

• Train and inference RAG evaluation model to predict not only binary labels, but also multiclass labels for the Correctness/Faithfulness/Relevance estimation
• Automatic threshold selection supports multiclass labels

4. Detailed usage examples

• Notebook for the basic RuRAGE functionality (preparing metric reports for the Correctness, Faithfulness, Relevance tasks)
• Notebook for the RAG evaluation ensemble training and inferencing.

5. Different bug fixes in the metric calculation

Basic functionality

1. Different types of RAG evaluation:

• Correctness – how well does the model's response follow the context
• Faithfulness – how well does the model follow the context
• Relevance – how well does the model's answer relate to the question

2. Metrics to build evaluation

a) Deterministic Metrics:

• ROUGE
• BLEU
• Bigram overlap Precision
• Bigram overlap Recall
• Bigram overlap F1
• Unigram overlap Precision
• Unigram overlap Recall
• Unigram overlap F1

b) Model-based Metrics:

• NLI Scores using Transformer models
• Cosine Similarity using Transformer models