Revisiting Challenges and Hazards in Large Language Model Evaluation

Abstract

In the age of large language models, artificial intelligence’s goal has evolved to assist humans in unprecedented ways. As LLMs integrate into society, the need for comprehensive evaluations increases. These systems’ real-world acceptance depends on their knowledge, reasoning, and argumentation abilities. However, inconsistent standards across domains complicate evaluations, making it hard to compare models and understand their pros and cons. Our study focuses on illuminating the evaluation processes for these models. We examine recent research, tracking current trends to ensure evaluation methods match the field’s rapid progress requirements. We analyze key evaluation dimensions, aiming to deeply understand factors affecting models performance. A key aspect of our work is identifying and compiling major performance challenges and hazards in evaluation, an area not extensively explored yet. This approach is necessary for recognizing the potential and limitations of these AI systems in various domains of the evaluation.