Improving Your Model Ranking on Chatbot Arena by Vote Rigging

Chatbot Arena is a popular platform for evaluating LLMs by pairwise battles, where users vote for their preferred response from two randomly sampled anonymous models. While Chatbot Arena is widely regarded as a reliable LLM ranking leaderboard, we show that crowdsourced voting can be rigged to improve (or decrease) the ranking of a target model m_{t}. We first introduce a straightforward target-only rigging strategy that focuses on new battles involving m_{t}, identifying it via watermarking or a binary classifier, and exclusively voting for m_{t} wins. However, this strategy is practically inefficient because there are over 190 models on Chatbot Arena and on average only about 1\% of new battles will involve m_{t}. To overcome this, we propose omnipresent rigging strategies, exploiting the Elo rating mechanism of Chatbot Arena that any new vote on a battle can influence the ranking of the target model m_{t}, even if m_{t} is not directly involved in the battle. We conduct experiments on around 1.7 million historical votes from the Chatbot Arena Notebook, showing that omnipresent rigging strategies can improve model rankings by rigging only hundreds of new votes. While we have evaluated several defense mechanisms, our findings highlight the importance of continued efforts to prevent vote rigging. Our code is available at this https URL