Some reliable and trendy evaluation metrics are: 1. Perplexity Perplexity measures how well a language model predicts a
Posted: Sun Jan 19, 2025 5:49 am
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.
4. METEOR
METEOR (Metric for Evaluation of Translation with Explicit ORdering) goes beyond exact word matching. It takes into account synonyms, stems, and paraphrases to assess the similarity between the generated text and the reference text. This metric aims to get closer to human judgment.
Example: If your model outputs "The feline was resting on the rug" and the reference is "The germany whatsapp number data cat was resting on the rug", METEOR would give it a higher score than BLEU because it recognizes that "feline" is a synonym for "cat" and that "rug" and "carpet" have similar meanings.
This makes METEOR especially useful for capturing the nuances of language.
5. BERTScore
BERTScore evaluates text similarity based on contextual embeddings derived from models such as BERT (Bidirectional Encoder Representations from Transformers). It focuses more on meaning than exact word matching, allowing for better assessment of semantic similarity .
Example: When comparing the sentences “The car ran down the road” and “The vehicle accelerated down the street,” BERTScore analyzes the underlying meanings rather than just the word choice.
Although the words differ, the general ideas are similar, resulting in a high BERTScore that reflects the effectiveness of the generated content.
6. Human evaluation
Human evaluation remains a crucial aspect of LLM evaluation. It involves human judges who assess the quality of the model output based on several criteria such as fluency and relevance . Techniques such as Likert scales and A/B testing can be used to gather feedback.
Example: After generating responses from a customer support chatbot, human evaluators could rate each response on a scale of 1 to 5. For example, if the chatbot provides a clear and helpful response to a customer query, it might receive a 5, while a vague or confusing response might get a 2.
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.
4. METEOR
METEOR (Metric for Evaluation of Translation with Explicit ORdering) goes beyond exact word matching. It takes into account synonyms, stems, and paraphrases to assess the similarity between the generated text and the reference text. This metric aims to get closer to human judgment.
Example: If your model outputs "The feline was resting on the rug" and the reference is "The germany whatsapp number data cat was resting on the rug", METEOR would give it a higher score than BLEU because it recognizes that "feline" is a synonym for "cat" and that "rug" and "carpet" have similar meanings.
This makes METEOR especially useful for capturing the nuances of language.
5. BERTScore
BERTScore evaluates text similarity based on contextual embeddings derived from models such as BERT (Bidirectional Encoder Representations from Transformers). It focuses more on meaning than exact word matching, allowing for better assessment of semantic similarity .
Example: When comparing the sentences “The car ran down the road” and “The vehicle accelerated down the street,” BERTScore analyzes the underlying meanings rather than just the word choice.
Although the words differ, the general ideas are similar, resulting in a high BERTScore that reflects the effectiveness of the generated content.
6. Human evaluation
Human evaluation remains a crucial aspect of LLM evaluation. It involves human judges who assess the quality of the model output based on several criteria such as fluency and relevance . Techniques such as Likert scales and A/B testing can be used to gather feedback.
Example: After generating responses from a customer support chatbot, human evaluators could rate each response on a scale of 1 to 5. For example, if the chatbot provides a clear and helpful response to a customer query, it might receive a 5, while a vague or confusing response might get a 2.