Back

This content provides a comprehensive survey and analysis of language model (LLM) evaluation. It begins by discussing the rapid progress of advanced LLMs and the need for robust evaluation methods to understand their capabilities and align them with human values. The survey then explores various aspects of LLM evaluation, including core capabilities, alignment evaluation, safety evaluation, potential applications, and popular benchmark evaluations.

The core capabilities of LLMs are discussed in depth, including their knowledge base, reasoning abilities, and performance across different domains. It highlights the strengths and limitations of LLMs in understanding complex concepts, recalling formulas, and completing logical chains accurately. The survey also discusses the phenomenon of LLMs’ knowledge utilization and how it differs from human cognition.

The survey emphasizes the importance of alignment evaluation, which ensures that LLMs’ behaviors align with human values. It examines the risks associated with LLMs, including power-seeking behaviors, and the need for in-depth risk evaluation. It also discusses the evaluation of LLMs as autonomous agents, their performance in different environments, and the potential applications of LLMs in various domains.

Safety evaluation is another crucial aspect of LLM evaluation, which includes assessing ethical concerns, biases, toxicity, and truthfulness. It highlights the challenges of evaluating LLMs’ biases and the importance of avoiding harmful outputs. The survey also explores evaluation methods for truthfulness and ethical considerations when deploying LLMs.

The potential applications of LLMs are discussed across domains such as biology, education, law, computer science, and finance. The survey highlights the performance of advanced LLMs in specific domains and their adaptability in addressing challenges. It also emphasizes the need for multilingual LLMs and the impact of model size on performance.

The survey provides an overview of popular benchmark evaluations for LLMs, including leaderboards, evaluation frameworks, and evaluation arenas. It discusses the flexibility and adaptability of benchmark evaluations, the importance of instruction tuning, and the effectiveness of few-shot settings. The survey also highlights the need for dynamic evaluation, continuous benchmark evolution, and enhancement-oriented evaluation for LLMs.

In conclusion, the survey acknowledges the rapid progress of LLMs and the need for robust evaluation methods. It provides comprehensive insights into the core capabilities, alignment evaluation, safety evaluation, potential applications, and popular benchmark evaluations for LLMs. The survey encourages the controlled advancement of LLMs and the development of evaluation methods that ensure their safe, reliable, and beneficial applications in various domains.

Words: 391