Evaluating Language Models with Holistic Methods: A Comprehensive Review

LLMEval-1 is a multidisciplinary evaluation framework designed to assess the understanding of natural language processing (NLP) models, particularly those based on deep learning techniques. The evaluation framework consists of three levels: fluency, informativeness, and logical coherence, each with its own set of metrics for assessment. These metrics are used to objectively measure the accuracy and consistency of different annotation methods in evaluating NLP models.
The article provides a detailed explanation of the LLMEval-1 evaluation framework, including its context, metrics, and appendix. The authors also discuss the importance of using a multidisciplinary approach to evaluate NLP models, as traditional evaluation methods may not provide a comprehensive assessment of their abilities.
The article highlights the need for a human-centric benchmark for evaluating foundation models, which are the building blocks of many NLP applications. The authors propose AGIEval, a new evaluation framework that focuses on human-centric evaluations and provides a more comprehensive assessment of NLP models.
The article concludes by emphasizing the importance of using multidisciplinary evaluation frameworks like LLMEval-1 and AGIEval to improve the understanding and performance of NLP models. By leveraging insights from cognitive psychology, linguistics, and other related fields, these frameworks can help develop more accurate and effective NLP models that can better serve human needs.