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arxiv:2412.04363

Challenges in Trustworthy Human Evaluation of Chatbots

Published on Dec 5
· Submitted by wentingzhao on Dec 6
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Abstract

Open community-driven platforms like Chatbot Arena that collect user preference data from site visitors have gained a reputation as one of the most trustworthy publicly available benchmarks for LLM performance. While now standard, it is tricky to implement effective guardrails to collect high-quality annotations from humans. In this paper, we demonstrate that three sources of bad annotations, both malicious and otherwise, can corrupt the reliability of open leaderboard rankings. In particular, we show that only 10\% of poor quality votes by apathetic (site visitors not appropriately incentivized to give correct votes) or adversarial (bad actors seeking to inflate the ranking of a target model) annotators can change the rankings of models by up to 5 places on the leaderboard. Finally, we discuss open challenges in ensuring high-quality human annotations.

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Eval platforms like Chatbot Arena attract users to provide preference votes. But what are the incentives of these users? Are they apathetic, or are they adversarial and just aiming to inflate their model rankings? We show 10% adversarial votes change the model rankings by a lot!
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