O.RI BME BENCHMARK MATCHING ENGINE



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Overview

• Main features:
• Determines the optimal benchmark model that asses models that could best answer the user query.
• Efficiently leverages faster and more cost-effective models without compromising quality.
• How we will build it:
• We will utilizes knowledge graphs, vector embeddings, or classification models to identify the best model and its inference endpoints from the registry for each atomic query (note: this is in progress, we have built a  classification model that has 96% accuracy  in our initial test, see the below technical section for the methodologies we experimented).
• We will maintains a comprehensive list of benchmark tests ( IFEval ,  BBH ,  MATH ,  GPQA ,  MuSR ,  MMLU ,  BFCL ) and the results of the models we host (note:  this is partly done ).
• We will build a data pipeline to dynamically update and host/ deploy the top ranked benchmark model collections in the inference endpoint registry in real-time (note:  this is partly done ).

Technical detail section

METHODOLODY TESTED

Approach 2.1

.1Data Preparation:

• A dataset is prepared where each prompt is associated with a specific LLM model, and the prompts have been pre-embedded using a sentence transformer model. This data is stored in a CSV file (filtered_data_with_embeddings.csv), which includes columns for both the prompts and their embeddings.

.2Loading the Data:

• The CSV file containing the prompts, their associated models, and embeddings is loaded into a pandas DataFrame. The embeddings are extracted into a separate NumPy array for easier processing.

.3New Prompt Embedding:

• When a new prompt is given, it is embedded using a pre-trained sentence transformer model (all-MiniLM-L6-v2).

.4Cosine Similarity Calculation:

• The cosine similarity between the new prompt's embedding and each embedding in the dataset is computed. This measures the similarity between the new prompt and the prompts in the dataset.

.5Model Selection:

• The index of the highest cosine similarity value is identified, which corresponds to the most similar prompt in the dataset. The model associated with this prompt is selected as the best model to handle the new prompt.

.6Output:

• The name of the best model is returned and displayed, providing a recommendation for which LLM to use for the new prompt.

Drawbacks of the Approach

.1Dependency on Dataset Quality:

• The accuracy of the model selection heavily depends on the quality and diversity of the dataset. If the dataset lacks prompts similar to the new ones, the routing might not work well.
• The embeddings used in the dataset must be representative of the types of prompts the models will encounter. If they are not, the routing decision might be suboptimal.

.2Limited Flexibility:

• The model selection is based purely on the similarity of embeddings, without considering other factors like the complexity of the prompt, specific model strengths, or the required response style.
• If a model is particularly good at certain tasks but these tasks are underrepresented in the dataset, the approach may not select the best model for those tasks.

.3Scalability Issues:

• As the dataset grows, computing cosine similarities between the new prompt's embedding and all dataset embeddings might become computationally expensive. This could lead to slow response times, especially in real-time applications.
• The entire dataset must be loaded into memory, which could be a problem for very large datasets.

.4Single-Point Prediction:

• The method selects only the most similar prompt, which means it does not consider other potentially relevant prompts that might suggest different models. It could be beneficial to consider a weighted average of similarities or top-N similar prompts to make a more informed decision.

.5Overfitting to Specific Prompts:

• The approach might overfit to the prompts present in the dataset, making it less effective for generalizing to new, unseen types of prompts. If the prompt types in the dataset are too narrow, the model might perform poorly on more diverse inputs.

.6Lack of Contextual Adaptation:

• The current approach does not adapt to contextual changes or dynamic updates in the dataset or models. If new models are introduced or existing ones are fine-tuned, the dataset needs to be manually updated, which is not optimal for environments with frequent changes.

.7No Confidence Measure:

• The method does not provide a confidence score or threshold for when the selected model might not be reliable, which could be critical in applications requiring high precision.

Approach 2.2

Analysis of the Approach

.1Approach:

• TF-IDF was used to vectorize prompts, transforming text data into numerical representations.
• An XGBoost classifier was trained to predict the best model based on the TF-IDF vectors.

.2Performance Insights:

• Accuracy: The overall accuracy is only 8%, which suggests the model is not performing well in distinguishing between classes (models).
• Precision, Recall, F1-Score: Most classes have near-zero values for precision, recall, and F1-score, indicating the model is failing to make correct predictions across nearly all classes.
• Macro and Weighted Averages: Both are low, reinforcing the poor general performance across all classes.

Drawbacks of the TF-IDF + XGBoost Approach

.1High Dimensionality and Sparsity:

• TF-IDF often results in high-dimensional and sparse vectors, which may not capture the semantic relationships between prompts effectively, leading to poor performance in downstream tasks like classification.

.2Inadequate Feature Representation:

• TF-IDF captures word frequency but not the contextual or semantic meaning of the text, which is crucial when routing prompts to LLMs. It fails to account for nuanced differences that embeddings handle better.

.3Model Complexity and Training:

• XGBoost is a powerful model but can struggle with high-dimensional sparse data, especially when the target classes are numerous and imbalanced, leading to ineffective learning.

.4Class Imbalance:

• The data appears highly imbalanced, with some classes having very few samples. XGBoost, without proper handling of class imbalance, might focus more on the majority classes, leading to poor generalization.

.5Lack of Contextual Learning:

• Unlike embeddings that consider the entire context of the text, TF-IDF ignores the word order and context, making it less suitable for understanding complex prompts.

Conclusion

Approach 2.3

• Model Selection: The approach utilizes transformer-based models, specifically BERT and DistilBERT, known for their strong contextual understanding of language, to classify prompts into categories that correspond to different LLM models.
• Data Preparation: Prompts are labeled with their corresponding target LLM models, creating a multi-class classification problem. This dataset is used to train the classifiers.
• Training Process:
• The models are fine-tuned on the labeled dataset to learn patterns and distinctions between different prompt types associated with each LLM model.
• The training involves minimizing the loss function over several epochs, adjusting the model weights to improve classification accuracy.
• Evaluation Metrics:
• The models are evaluated using metrics such as accuracy, precision, recall, and F1-score, with a focus on how well each class (LLM model) is identified by the classifier.
• Validation accuracy and training loss are monitored over epochs to assess the model's performance and convergence.
• Inference:
• For a new prompt, the classifier predicts the most likely LLM model by analyzing the contextual and semantic features of the text, aiming to match it to the most suitable class based on training.



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Drawbacks

.1Low Overall Performance:

• The model achieved an accuracy of only 10%, indicating that it struggles significantly with correctly classifying the prompts across multiple classes.

.2Imbalanced Performance Across Classes:

• Some classes, like "RWKV-4-Raven-14B" and "oasst-pythia-12b," perform well, with relatively high precision and recall, while many other classes have near-zero values, indicating a failure to learn meaningful distinctions.

.3High Variability in Validation Accuracy:

• The validation accuracy graph shows fluctuations across epochs, suggesting potential instability in the model’s training process or overfitting issues.

.4Class Imbalance:

• Several classes have a very low number of samples, leading to poor model performance for these underrepresented classes, which is evident in the near-zero scores for many categories.

.5Limited Contextual Understanding:

• Despite using transformer-based models, the complexity and variability of LLM model-specific prompts seem to pose challenges beyond simple contextual comprehension, highlighting the need for more sophisticated fine-tuning or data augmentation strategies.

Approach

.1Model Design: A DistilBERT-based regressor is fine-tuned to predict MMLU scores, which indicate the level of difficulty required to solve a given prompt. The model consists of a DistilBERT encoder, a dropout layer, and a linear regressor to output the score.
.2Data Preparation: The data includes prompts and corresponding MMLU scores, normalized for consistency. Prompts are tokenized using the DistilBERT tokenizer and split into training and validation sets.
.3Training Setup: A custom dataset class and dataloaders are defined for efficient batching. The training involves optimizing the model using AdamW, with loss monitored using MSELoss. Learning rate adjustments are handled by a scheduler that reduces the learning rate when validation performance plateaus.
.4Evaluation: The model's performance is tracked using RMSE on the validation set, with logs recorded in TensorBoard. Training loss and validation RMSE are plotted over epochs to assess model convergence and generalization.
.5Prediction: A prediction function is implemented to estimate the MMLU score for new prompts, using the trained model to determine the complexity level required to solve the prompt.

Drawbacks

.1Limited by Data Quality: The accuracy of predicted MMLU scores depends heavily on the quality and representativeness of the training data. Inconsistent or biased data can lead to poor predictions.
.2Computational Intensity: Fine-tuning transformer models like DistilBERT is computationally expensive, especially when scaling to large datasets or deploying in real-time environments.
.3Overfitting Risk: The model might overfit to specific prompt types or patterns seen during training, which can reduce its generalizability to unseen or diverse prompts.
.4Scalability: The current approach may struggle to scale effectively across significantly larger datasets or higher-dimensional prompts without further optimization or model adjustments.
.5Interpretability: Regression models based on transformer outputs can be challenging to interpret, making it difficult to understand why certain MMLU scores are predicted, especially when compared to simpler heuristic methods.



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Approach 2.5

Predicting Normalized MMLU Scores: Comparing XGBoost and DistilBERT Models for Optimal Performance

Approach

.1Data Normalization: MMLU scores were normalized to ensure consistent scaling, enhancing the performance of both models.
.2Model Training:

• XGBoost: A tree-based model trained on tabular data with normalized scores, known for its speed and performance in structured data regression tasks.
• DistilBERT: A transformer-based regression model fine-tuned on the prompts to predict normalized MMLU scores, leveraging deep contextual understanding.

.3Evaluation: Both models were trained separately and evaluated on their ability to predict the normalized MMLU scores, with DistilBERT capturing text semantics and XGBoost handling numerical features efficiently.

Drawbacks

.1Feature Dependency: Each model has strengths depending on the type of input data—DistilBERT on text, XGBoost on numerical/tabular data—limiting their independent versatility.
.2Training Complexity: Managing two distinct models requires separate optimization, tuning, and maintenance efforts.
.3Resource Intensity: Fine-tuning DistilBERT can be resource-intensive, and XGBoost may require extensive hyperparameter tuning for optimal performance.

Approach 2.6

Approach

.1Data Preparation: The task data is organized into a structured format, where each category contains tasks with associated benchmark scores and model types, helping to guide model selection.
.2Zero-Shot Classification: The facebook/bart-large-mnli model is employed to classify task descriptions into predefined categories without specific task training, using a zero-shot classification pipeline.
.3Task and Model Selection:

• For each task description, the model predicts the category with the highest confidence score.
• Within the predicted category, the approach attempts to identify the specific task, matching it to its benchmark score and the optimal model type designated for that task.

.4Model Assignment: The identified task details guide the selection of the best model type for the job, effectively routing tasks to the most suitable model based on the description and benchmark data.

Conclusion

Approach 2.7

Approach

.1Data Structure: Task data is maintained in a dictionary with categories containing tasks, benchmark scores, and suggested model types.
.2Zero-Shot Classification: Using the facebook/bart-large-mnli model, task descriptions are classified into predefined categories, and the most relevant category is predicted based on confidence scores.
.3Task and Model Selection:

• For each task, the predicted category is used to find the most specific matching task and its associated details.
• The approach identifies the best model type for the task from predefined data, streamlining model assignment.

.4Model Evaluation Extension:

• The approach is expanded to include multiple classifiers: MiniLM, ALBERT, DeBERTa, XLM-RoBERTa, and ELECTRA.
• Each classifier is tested on the task description, and their performances are compared to determine which model best suits the task.
• The best classifier is selected based on accuracy, confidence scores, and alignment with the task's benchmark requirements.

.5Enhanced Model Selection:

• The task is routed to the model with the highest performance, ensuring that the task description aligns with the strengths of the selected classifier.
• This dynamic selection process allows the system to choose the most appropriate model for each job rather than sticking to a single static model.

Conclusion

Approach 2.8

BERT Model Evaluation

• The BERT model was trained on a synthetic dataset to classify various NLP tasks, including Emotion_Recognition_in_Text, Code_Generation, Creative_Writing, and more.
• The training involved fine-tuning the BERT model on the task-specific data, with training and validation losses monitored throughout.
• The evaluation was performed using metrics such as precision, recall, and F1-score, along with a confusion matrix to identify class-specific performance.

• Strengths: BERT performed exceptionally well on tasks like Code_Generation, Creative_Writing, and Fact-Checking, with high precision and recall scores. This indicates BERT’s strong contextual understanding and ability to handle tasks that require deep language comprehension.
• Weaknesses: The model struggled with Emotion_Recognition_in_Text, achieving poor scores in this class, likely due to insufficient data or the complexity of recognizing nuanced emotional content. Additionally, the slight underperformance in Fact-Based_QA suggests a need for more focused fine-tuning or data augmentation in specific categories.
• Overall Performance: BERT achieved an overall accuracy of 94%, demonstrating its capacity for handling diverse NLP tasks but highlighting areas that could benefit from further refinement.

XGBoost Model Evaluation

• The XGBoost model was trained using the same synthetic dataset to classify the same set of NLP tasks. XGBoost, being a gradient-boosting decision tree model, relies on feature-based representations rather than contextual language understanding.
• The evaluation focused on precision, recall, and F1-score, supported by a confusion matrix to highlight class-specific performance.

• Strengths: XGBoost showed high performance across most classes, achieving perfect scores in categories like Code_Generation, Dialogue_Generation, and Fact-Checking. The model’s structured approach allowed it to efficiently handle tasks with clear patterns or features.
• Weaknesses: Similar to BERT, XGBoost struggled with Emotion_Recognition_in_Text, indicating this class's inherent complexity and the possible need for additional data or feature engineering. The model also showed slight misclassifications in Creative_Writing, suggesting limitations in capturing creative or nuanced content compared to BERT.
• Overall Performance: XGBoost achieved a slightly higher accuracy of 96%, indicating robust performance, particularly in classes that align well with structured feature patterns.

• Average Training Loss: 0.7933
• Average Validation Loss: 0.5855

Classification Report:

• Accuracy: 0.94
• Macro Avg: Precision: 0.88, Recall: 0.87, F1-Score: 0.87
• Weighted Avg: Precision: 0.94, Recall: 0.94, F1-Score: 0.94



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Confusion Matrix:



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XGBoost Model

Classification Report:

• Accuracy: 0.96
• Macro Avg: Precision: 0.89, Recall: 0.90, F1-Score: 0.89
• Weighted Avg: Precision: 0.95, Recall: 0.96, F1-Score: 0.95