  We propose LLM-Eval, a unified multi-dimensional automatic evaluation method
for open-domain conversations with large language models (LLMs). Existing
evaluation methods often rely on human annotations, ground-truth responses, or
multiple LLM prompts, which can be expensive and time-consuming. To address
these issues, we design a single prompt-based evaluation method that leverages
a unified evaluation schema to cover multiple dimensions of conversation
quality in a single model call. We extensively evaluate the performance of
LLM-Eval on various benchmark datasets, demonstrating its effectiveness,
efficiency, and adaptability compared to state-of-the-art evaluation methods.
Our analysis also highlights the importance of choosing suitable LLMs and
decoding strategies for accurate evaluation results. LLM-Eval offers a
versatile and robust solution for evaluating open-domain conversation systems,
streamlining the evaluation process and providing consistent performance across
diverse scenarios.
