Implemented LLM performance test cases #76
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
The tests cover different aspects for testing LLM performance. Some of them are as follows : 1. Length: Measures the absolute difference in length between the ground truth and model prediction. 2. Jaccard similarity: Calculates the Jaccard similarity score between the ground truth and model prediction sets. 3. Dot product similarity: Measures the similarity between the ground truth and model prediction embeddings using dot product. 4. ROUGE score: Computes the ROUGE-1 score between the ground truth and model prediction. 5. Word overlap: Calculates the percentage of overlapping words between the ground truth and model prediction after removing stop words. 6. Part-of-speech composition: Analyzes the percentage of verbs, adjectives, and nouns in the model prediction.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
The tests cover different aspects for testing LLM performance. Some of them are as follows :
Length: Measures the absolute difference in length between the ground truth and model prediction.
Jaccard similarity: Calculates the Jaccard similarity score between the ground truth and model prediction sets.
Dot product similarity: Measures the similarity between the ground truth and model prediction embeddings using dot product.
ROUGE score: Computes the ROUGE-1 score between the ground truth and model prediction.
Word overlap: Calculates the percentage of overlapping words between the ground truth and model prediction after removing stop words.
Part-of-speech composition: Analyzes the percentage of verbs, adjectives, and nouns in the model prediction.