CosineSimilarityComparison

Computes pairwise cosine similarities between model embeddings and visualizes the results through bar charts, alongside compiling a comprehensive table of descriptive statistics for each model pair.

Purpose: This function is designed to analyze and compare the embeddings produced by different models using Cosine Similarity. Cosine Similarity, a measure calculating the cosine of the angle between two vectors, is widely used to determine the alignment or similarity between vectors in high-dimensional spaces, such as text embeddings. This analysis helps to understand how similar or different the models’ predictions are in terms of embedding generation.

Test Mechanism: The function begins by computing the embeddings for each model using the provided dataset. It then calculates the cosine similarity for every possible pair of models, generating a similarity matrix. Each element of this matrix represents the cosine similarity between two model embeddings. The function flattens this matrix and uses it to create a bar chart for each model pair, visualizing their similarity distribution. Additionally, it compiles a table with descriptive statistics (mean, median, standard deviation, minimum, and maximum) for the similarities of each pair, including a reference to the compared models.

Signs of High Risk:

• A high concentration of cosine similarity values close to 1 could suggest that the models are producing very similar embeddings, which could be a sign of redundancy or lack of diversity in model training or design.
• Conversely, very low similarity values near -1 indicate strong dissimilarity, potentially highlighting models that are too divergent, possibly focusing on very different features of the data.

Strengths:

• Enables detailed comparisons between multiple models’ embedding strategies through visual and statistical means.
• Helps identify which models produce similar or dissimilar embeddings, useful for tasks requiring model diversity.
• Provides quantitative and visual feedback on the degree of similarity, enhancing interpretability of model behavior in embedding spaces.

Limitations:

• The analysis is confined to the comparison of embeddings and does not assess the overall performance of the models in terms of their primary tasks (e.g., classification, regression).
• Assumes that the models are suitable for generating comparable embeddings, which might not always be the case, especially across different types of models.
• Interpretation of results is heavily dependent on the understanding of Cosine Similarity and the nature of high-dimensional embedding spaces.