Revisiting Benchmark and Assessment: An Agent-based Exploratory Dynamic Evaluation Framework for LLMs

While various vertical domain large language models (LLMs) have been developed, the challenge of automatically evaluating their performance across different domains remains significant. Current benchmark-based evaluation methods exhibit rigid, aimless interactions and rely on pre-collected static datasets that are costly to build, inflexible across domains, and misaligned with practical user needs. To address this issue, we revisit the evaluation components and introduce two concepts: Benchmark+, which extends traditional question-answer benchmark into a more flexible "strategy-criterion" format; and Assessment+, which enhances the interaction process, enabling deeper exploration and supporting both quantitative metrics and qualitative insights. These concepts capture the nuanced behaviors of LLMs through richer, multi-turn interactions. We propose an agent-based evaluation framework called TestAgent, which implements these concepts through retrieval augmented generation and reinforcement learning. Experiments on tasks ranging from constructing vertical domain evaluation to activating existing benchmarks demonstrate the effectiveness of TestAgent across various scenarios. We believe this work offers an interesting perspective on automatic evaluation for LLMs.