Conceptos Básicos
This paper provides a comprehensive exploration of evaluation metrics for Large Language Models (LLMs), offering insights into the selection and interpretation of metrics currently in use, and showcasing their application through recently published biomedical LLMs.
Resumen
The paper offers a comprehensive exploration of evaluation metrics for Large Language Models (LLMs), providing insights into the selection and interpretation of metrics currently in use. It categorizes the metrics into three types: Multiple-Classification (MC), Token-Similarity (TS), and Question-Answering (QA) metrics.
For MC metrics, the paper explains the mathematical formulations and statistical interpretations of Accuracy, Recall, Precision, F1-score, micro-F1, and macro-F1. It highlights the advantages of macro-F1 in addressing the limitations of accuracy metrics.
For TS metrics, the paper covers Perplexity, BLEU, ROUGE-n, ROUGE-L, METEOR, and BertScore. It discusses the statistical interpretations of these metrics and their strengths in evaluating the quality of generated texts.
For QA metrics, the paper explains Strict Accuracy (SaCC), Lenient Accuracy (LaCC), and Mean Reciprocal Rank (MRR), which are tailored for Question Answering tasks.
The paper also showcases the application of these metrics in evaluating recently developed biomedical LLMs, providing a comprehensive summary of benchmark datasets and downstream tasks associated with each LLM.
Finally, the paper discusses the strengths and weaknesses of the existing metrics, highlighting the issues of imperfect labeling and the lack of statistical inference methods. It suggests borrowing ideas from diagnostic studies to address these challenges and improve the reliability of LLM evaluations.
Estadísticas
"Over 3000 new articles in peer-reviewed journals are published daily."
"ChatGPT, also namely GPT3.5, has demonstrated its remarkable ability to generate coherent sequences of words and engage in conversational interactions."
Citas
"LLMs present a significant opportunity for tasks such as generating scientific texts, answering questions, and extracting core information from articles."
"The proliferation of LLMs has prompted the emergence of reviews aimed at providing insights into their development and potential applications."
"Evaluation encompasses various aspects, including downstream tasks, criteria, benchmark datasets, and metrics."