Metrics for the evaluation of the LLM

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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 be higher, indicating thailand whatsapp number data 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.

3. F1 Score
The LLM evaluation metric of F1 score is primarily for classification tasks. It measures the balance between precision (the accuracy of positive predictions) and recall (the ability to identify all relevant instances).

Its range is from 0 to 1, where a score of 1 indicates 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 includes "The sky is green" (false positive), the F1 score will take into account the relevance of both the correct and incorrect answer.

This metric helps ensure a balanced evaluation of model performance.