Metrics for the evaluation of the LLM
Posted: Sun Jan 19, 2025 5:49 am
Some reliable and trendy evaluation metrics are:
1. Perplexity
Perplexity measures how well a language model predicts a sequence of words. Essentially, it indicates the model's uncertainty about the next word in a sentence. A lower perplexity score means that the model is more confident in its predictions, which translates to better performance.
Example: Imagine a model generates text from the cue “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.
However, if you suggest an unrelated word, such as "spaceship," the perplexity score will germany whatsapp number data be higher, indicating that the model is having difficulty predicting sensible text.
2. BLEU Score
The BLEU (Bilingual Evaluation Understudy) score is primarily used to evaluate machine translation and assess text generation.
Measures how many n-grams (contiguous sequences of n items from a given text sample) in the result overlap with those in one or more reference texts. The score ranges from 0 to 1, with higher scores indicating better performance.
Example: If your model outputs 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 the shared n-grams.
A high score indicates that the generated phrase matches the reference, while a low score might suggest that the generated result does not align well.
1. Perplexity
Perplexity measures how well a language model predicts a sequence of words. Essentially, it indicates the model's uncertainty about the next word in a sentence. A lower perplexity score means that the model is more confident in its predictions, which translates to better performance.
Example: Imagine a model generates text from the cue “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.
However, if you suggest an unrelated word, such as "spaceship," the perplexity score will germany whatsapp number data be higher, indicating that the model is having difficulty predicting sensible text.
2. BLEU Score
The BLEU (Bilingual Evaluation Understudy) score is primarily used to evaluate machine translation and assess text generation.
Measures how many n-grams (contiguous sequences of n items from a given text sample) in the result overlap with those in one or more reference texts. The score ranges from 0 to 1, with higher scores indicating better performance.
Example: If your model outputs 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 the shared n-grams.
A high score indicates that the generated phrase matches the reference, while a low score might suggest that the generated result does not align well.