model is having trouble predicting meaningful text
Posted: Sun Jan 12, 2025 8:10 am
Indicators for LLM assessment
Here are some reliable and trendy evaluation indicators:
1. Perplexity
Perplexity measures how well a language model predicts a sequence of words . It essentially indicates how uncertain the model is about the next word in a sentence. A lower perplexity score means the model is more confident in its predictions, which translates to better performance.
Example: Let's say a doctor database model generates text from the prompt "The cat sat on the." If it predicts a high probability for words like "mat" and "floor," it understands the context well, resulting in a low perplexity score.
On the other hand, if it suggests a word that is unrelated to the context, such as "spaceship," the perplexity score will be higher, indicating that the .
2. BLUE score
The BLEU (Bilingual Evaluation Understudy) score is mainly used to evaluate machine translation and text generation.
It measures the number of n-grams (contiguous sequences of n elements from a given text sample) in the result that overlap with those of one or more reference texts . The score ranges from 0 to 1, with higher scores indicating better performance.
Example: If your model generates the sentence "The quick brown fox jumps over the lazy dog" and the reference text is "A quick brown fox jumps over a lazy dog", BLEU will compare shared n-grams.
A high score indicates that the generated sentence closely matches the reference, while a lower score may suggest that the generated output is not well aligned.
3. F1 Score
The F1 score is an LLM evaluation metric primarily intended for classification tasks. It measures the balance between precision (the accuracy of positive predictions) and recall (the ability to identify all relevant instances).
It ranges from 0 to 1, with a score of 1 indicating perfect accuracy.
Example: In a question answering task, if the model is asked "What color is the sky?" and it answers "The sky is blue" (true positive) but also "The sky is green" (false positive), the F1 score will take into account the relevance of both the correct answer and the incorrect answer.
Here are some reliable and trendy evaluation indicators:
1. Perplexity
Perplexity measures how well a language model predicts a sequence of words . It essentially indicates how uncertain the model is about the next word in a sentence. A lower perplexity score means the model is more confident in its predictions, which translates to better performance.
Example: Let's say a doctor database model generates text from the prompt "The cat sat on the." If it predicts a high probability for words like "mat" and "floor," it understands the context well, resulting in a low perplexity score.On the other hand, if it suggests a word that is unrelated to the context, such as "spaceship," the perplexity score will be higher, indicating that the .
2. BLUE score
The BLEU (Bilingual Evaluation Understudy) score is mainly used to evaluate machine translation and text generation.
It measures the number of n-grams (contiguous sequences of n elements from a given text sample) in the result that overlap with those of one or more reference texts . The score ranges from 0 to 1, with higher scores indicating better performance.
Example: If your model generates the sentence "The quick brown fox jumps over the lazy dog" and the reference text is "A quick brown fox jumps over a lazy dog", BLEU will compare shared n-grams.A high score indicates that the generated sentence closely matches the reference, while a lower score may suggest that the generated output is not well aligned.
3. F1 Score
The F1 score is an LLM evaluation metric primarily intended for classification tasks. It measures the balance between precision (the accuracy of positive predictions) and recall (the ability to identify all relevant instances).
It ranges from 0 to 1, with a score of 1 indicating perfect accuracy.
Example: In a question answering task, if the model is asked "What color is the sky?" and it answers "The sky is blue" (true positive) but also "The sky is green" (false positive), the F1 score will take into account the relevance of both the correct answer and the incorrect answer.