TreeEval: Benchmark-Free Evaluation of Large Language Models through Tree Planning

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 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 7B, 13B, and 33B, 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.