Evaluating Large Language Models (LLMs): A Standard Set of Metrics for Accurate Assessment

Large Language Models (LLMs) are a type of artificial intelligence model that can generate human-like text. They are trained on large amounts of text data and can be used for a variety of natural language processing tasks, such as language translation, question answering, and text generation.

Evaluating LLMs is important to ensure that they are performing well and generating high-quality text. This is especially important for applications where the generated text is used to make decisions or provide information to users.

Standard Set of Metrics for Evaluating LLMs

There are several standard metrics for evaluating LLMs, including perplexity, accuracy, F1-score, ROUGE score, BLEU score, METEOR score, question answering metrics, sentiment analysis metrics, named entity recognition metrics, and contextualized word embeddings. These metrics help in assessing LLM performance by measuring various aspects of the generated text, such as fluency, coherence, accuracy, and relevance.

Perplexity

Perplexity is a measure of how well a language model predicts a sample of text. It is calculated as the inverse probability of the test set normalized by the number of words.

Perplexity can be calculated using the following formula: perplexity = 2^(-log P(w1,w2,...,wn)/n), where P(w1,w2,...,wn) is the probability of the test set and n is the number of words in the test set.

Imagine we have a language model that is trained on a corpus of text and we want to evaluate its performance on a test set. The test set consists of 1000 words, and the language model assigns a probability of 0.001 to each word. The perplexity of the language model on the test set is 2^(-log(0.001*1000)/1000) = 31.62.

Accuracy

Accuracy is a measure of how well a language model makes correct predictions. It is calculated as the number of correct predictions divided by the total number of predictions.

Accuracy can be calculated using the following formula: accuracy = (number of correct predictions) / (total number of predictions).

Suppose we have a language model that is trained to classify images of cats and dogs. We test the model on a set of 100 images, of which 80 are cats and 20 are dogs. The model correctly classifies 75 cats and 15 dogs. The accuracy of the model is (75+15)/(80+20) = 0.9.

F1-score

F1-score is a measure of a language model's balance between precision and recall. It is calculated as the harmonic mean of precision and recall.

F1-score can be calculated using the following formula: F1-score = 2 (precision recall) / (precision + recall), where precision is the number of true positives divided by the number of true positives plus false positives, and recall is the number of true positives divided by the number of true positives plus false negatives.

Assume that we have a language model that is trained to identify spam emails. We test the model on a set of 100 emails, of which 80 are legitimate and 20 are spam. The model correctly identifies 15 spam emails and incorrectly identifies 5 legitimate emails as spam. The precision of the model is 15/(15+5) = 0.75, and the recall of the model is 15/(15+5) = 0.75. The F1-score of the model is 2*(0.75*0.75)/(0.75+0.75) = 0.75.

ROUGE score

Definition of ROUGE score ROUGE score is a measure of how well a language model generates text that is similar to reference texts. It is commonly used for text generation tasks such as summarization and paraphrasing.

How to calculate ROUGE score ROUGE score can be calculated using various methods, such as ROUGE-N, ROUGE-L, and ROUGE-W. These methods compare the generated text to one or more reference texts and calculate a score based on the overlap between them.

Suppose we have a language model that is trained to generate summaries of news articles. We test the model on a set of 100 news articles, and the generated summaries are compared to the actual summaries of the articles. The ROUGE score of the model is calculated based on the overlap between the generated summaries and the actual summaries.

BLEU score

BLEU score is a measure of how well a language model generates text that is fluent and coherent. It is commonly used for text generation tasks such as machine translation and image captioning.

BLEU score can be calculated by comparing the generated text to one or more reference texts and calculating a score based on the n-gram overlap between them.

Imagine we have a language model that is trained to generate captions for images. We test the model on a set of 100 images, and the generated captions are compared to the actual captions of the images. The BLEU score of the model is calculated based on the n-gram overlap between the generated captions and the actual captions.

METEOR score

METEOR score is a measure of how well a language model generates text that is accurate and relevant. It combines both precision and recall to evaluate the quality of the generated text.

How to calculate METEOR score METEOR score can be calculated by comparing the generated text to one or more reference texts and calculating a score based on the harmonic mean of precision and recall.

Suppose we have a language model that is trained to generate translations of sentences from one language to another. We test the model on a set of 100 sentences, and the generated translations are compared to the actual translations of the sentences. The METEOR score of the model is calculated based on the harmonic mean of precision and recall.

Question Answering Metrics

Question answering metrics are used to evaluate the ability of a language model to provide correct answers to questions. Common metrics include accuracy, F1-score, and Macro F1-score.

Question answering metrics can be calculated by comparing the generated answers to one or more reference answers and calculating a score based on the overlap between them.

Lets say we have a language model that is trained to answer questions about a given text. We test the model on a set of 100 questions, and the generated answers are compared to the actual answers. The accuracy, F1-score, and Macro F1-score of the model are calculated based on the overlap between the generated answers and the actual answers.

Sentiment Analysis Metrics

Sentiment analysis metrics are used to evaluate the ability of a language model to classify sentiments correctly. Common metrics include accuracy, weighted accuracy, and macro F1-score.

Sentiment analysis metrics can be calculated by comparing the generated sentiment labels to one or more reference labels and calculating a score based on the overlap between them.

Suppose we have a language model that is trained to classify movie reviews as positive or negative. We test the model on a set of 100 reviews, and the generated sentiment labels are compared to the actual labels. The accuracy, weighted accuracy, and macro F1-score of the model are calculated based on the overlap between the generated labels and the actual labels.

Named Entity Recognition Metrics

Named entity recognition metrics are used to evaluate the ability of a language model to identify entities correctly. Common metrics include accuracy, precision, recall, and F1-score.

Named entity recognition metrics can be calculated by comparing the generated entity labels to one or more reference labels and calculating a score based on the overlap between them.

Suppose we have a language model that is trained to identify people, organizations, and locations in a given text. We test the model on a set of 100 texts, and the generated entity labels are compared to the actual labels. The accuracy, precision, recall, and F1-score of the model are calculated based on the overlap between the generated labels and the actual labels.

Contextualized Word Embeddings

Contextualized word embeddings are used to evaluate the ability of a language model to capture context and meaning in word representations. They are generated by training the language model to predict the next word in a sentence given the previous words.

How to evaluate contextualized word embeddings Contextualized word embeddings can be evaluated by comparing the generated embeddings to one or more reference embeddings and calculating a score based on the similarity between them.

Lets say we have a language model that is trained to generate word embeddings for a given text. We test the model on a set of 100 texts, and the generated embeddings are compared to the actual embeddings. The evaluation can be done using various methods, such as cosine similarity and Euclidean distance.

Conclusion

The standard set of metrics for evaluating LLMs includes perplexity, accuracy, F1-score, ROUGE score, BLEU score, METEOR score, question answering metrics, sentiment analysis metrics, named entity recognition metrics, and contextualized word embeddings.

Importance of choosing the appropriate metrics for specific tasks It is important to choose the appropriate metrics for specific tasks to ensure that the LLM is evaluated accurately and comprehensively.

Future directions for LLM evaluation research Future research on LLM evaluation could focus on developing new metrics that better capture the human-like abilities of LLMs and their impact on end-users.

I hope this article provides a comprehensive overview of the standard set of metrics for evaluating LLMs and their importance in assessing LLM performance.

References:

(1) How to Evaluate LLMs: A Complete Metric Framework Microsoft Research. https://www.microsoft.com/en-us/research/group/experimentation-platform-exp/articles/how-to-evaluate-llms-a-complete-metric-framework/.

(2) Evaluating Large Language Models. https://toloka.ai/blog/evaluating-llms/.

(3) LLM Benchmarks: Guide to Evaluating Language Models. https://deepgram.com/learn/llm-benchmarks-guide-to-evaluating-language-models.

(4) How to Evaluate LLMs? Analytics Vidhya. https://www.analyticsvidhya.com/blog/2023/05/how-to-evaluate-a-large-language-model-llm/.