Large language models (LLMs) have gained significant attention in recent years due to their remarkable capabilities in natural language processing tasks. In this paper, the authors provide a comprehensive review of evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. They first provide an overview of evaluation tasks including general natural language processing tasks, reasoning, medical usage, ethics, education, natural and social sciences, agent applications and other areas. Then they dive into the evaluation methods and benchmarks used in assessing the performance of LLMs. The paper also discusses success and failure cases of LLMs in different tasks and highlights the importance of treating evaluation as an essential discipline to aid the development of more proficient LLMs. The paper concludes with a discussion on future challenges in LLMs evaluation.
Large language models (LLMs) are very smart computer programs that can understand and use human language. The authors of a study talk about different ways to test how well LLMs work. They look at what to test, where to test, and how to test the LLMs. They also talk about different areas where LLMs can be used, like medicine and science. They discuss how to measure the performance of LLMs and give examples of when they do well or not so well. They say it's important to keep testing LLMs so they can get even better in the future."
Definitions
- Large language models (LLMs): Smart computer programs that understand and use human language.
- Evaluation: Testing or checking something to see how well it works.
- Performance: How well something does its job.
- Proficient: Very good at doing something.
- Discipline: A field of study or practice.
A Comprehensive Review of Evaluation Methods for Large Language Models
Large language models (LLMs) have become increasingly popular in recent years due to their remarkable capabilities in natural language processing tasks. However, it is essential to evaluate these models before they can be deployed in real-world applications. In this paper, the authors provide a comprehensive review of evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate and how to evaluate.
What To Evaluate
The authors begin by providing an overview of evaluation tasks for LLMs including general natural language processing tasks such as sentiment analysis and text summarization; reasoning tasks such as question answering and machine translation; medical usage; ethics; education; natural and social sciences; agent applications and other areas. They also discuss the importance of evaluating not only accuracy but also fairness, interpretability, robustness and scalability when assessing the performance of LLMs.
Where To Evaluate
The paper then dives into the evaluation methods used in different contexts including static datasets like GLUE or SuperGLUE benchmarks that are commonly used for evaluating general-purpose NLP models as well as dynamic datasets like OpenAI’s GPT-2 which are designed specifically for testing large language models. The authors emphasize the importance of using appropriate datasets that reflect the domain or task at hand when evaluating LLMs.
How To Evaluate
The paper further discusses success and failure cases of LLMs in different tasks with examples from various domains such as healthcare or finance. It highlights the importance of treating evaluation as an essential discipline to aid the development of more proficient LLMs by emphasizing metrics such as precision/recall scores or perplexity values which measure model performance across multiple dimensions rather than relying solely on accuracy scores alone.
Conclusion
In conclusion, this paper provides a comprehensive review on evaluation methods for large language models covering what to evaluate, where to evaluate and how to evaluate them effectively across various domains. It emphasizes the need for proper assessment techniques that go beyond accuracy scores alone while taking into account factors such as fairness, interpretability, robustness and scalability when designing more proficient LLM systems in order to meet future challenges successfully.
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, educations, natural and social sciences, agent applications, and other areas. Secondly, we answer the `where' and `how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey.
