Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates
Authors: name: Xiaosen Zheng, name: Tianyu Pang, name: Chao Du, name: Qian Liu, name: Jing Jiang, name: Min Lin

Automatic LLM benchmarks, such as AlpacaEval 2.0, Arena-Hard-Auto, and
MT-Bench, have become popular for evaluating language models due to their
cost-effectiveness and scalability compared to human evaluation. Achieving high
win rates on these benchmarks can significantly boost the promotional impact of
newly released language models. This promotional benefit may motivate tricks,
such as manipulating model output length or style to game win rates, even
though several mechanisms have been developed to control length and disentangle
style to reduce gameability. Nonetheless, we show that even a "null model" that
always outputs a constant response (irrelevant to input instructions) can cheat
automatic benchmarks and achieve top-ranked win rates: an 86.5% LC win rate on
AlpacaEval 2.0; an 83.0 score on Arena-Hard-Auto; and a 9.55 score on MT-Bench.
Moreover, the crafted cheating outputs are transferable because we assume that
the instructions of these benchmarks (e.g., 805 samples of AlpacaEval 2.0) are
private and cannot be accessed. While our experiments are primarily
proof-of-concept, an adversary could use LLMs to generate more imperceptible
cheating responses, unethically benefiting from high win rates and promotional
impact. Our findings call for the development of anti-cheating mechanisms for
reliable automatic benchmarks. The code is available at
https://github.com/sail-sg/Cheating-LLM-Benchmarks.