TreeEval: Benchmark-Free Evaluation of Large Language Models through
  Tree Planning
Xiang Li Yunshi Lan Chao Yang
Recently, numerous new benchmarks have been established to evaluate the
performance of large language models (LLMs) via either computing a holistic
score or employing another LLM as a judge. However, these approaches suffer
from data leakage due to the open access of the benchmark and inflexible
evaluation process. To address this issue, we introduce $\textbf{TreeEval}$, a
benchmark-free evaluation method for LLMs that let a high-performance LLM host
an irreproducible evaluation session and essentially avoids the data leakage.
Moreover, this LLM performs as an examiner to raise up a series of questions
under a topic with a tree planing strategy, which considers the current
evaluation status to decide the next question generation and ensures the
completeness and efficiency of the evaluation process. We evaluate $6$ models
of different parameter sizes, including $7$B, $13$B, and $33$B, and ultimately
achieved the highest correlation coefficient with AlpacaEval2.0 using only
around $45$ questions. We also conduct more analysis to show the robustness and
reliability of TreeEval. Our code can be accessed via the provided
https://github.com/Ashura5/TreeEval.