Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences. To address this, we explore using strong LLMs as judges to evaluate these models on more open-ended questions. We examine the usage and limitations of LLM-as-a-judge, including position, verbosity, and self-enhancement biases, as well as limited reasoning ability, and propose solutions to mitigate some of them. We then verify the agreement between LLM judges and human preferences by introducing two benchmarks: MT-bench, a multi-turn question set; and Chatbot Arena, a crowdsourced battle platform. Our results reveal that strong LLM judges like GPT-4 can match both controlled and crowdsourced human preferences well, achieving over 80% agreement, the same level of agreement between humans. Hence, LLM-as-a-judge is a scalable and explainable way to approximate human preferences, which are otherwise very expensive to obtain.

Once aligned with humans, these chat models are strongly preferred by human users over the original, unaligned models on which they are built. However, the heightened user preference does not always correspond to improved scores on traditional LLM benchmarks

We argue that this discrepancy primarily arises due to existing evaluation that only measures LLMs’ core capability on a confined set of tasks (e.g., multi-choice knowledge or retrieval questions), without adequately assessing its alignment with human preference in open-ended tasks, such as the ability to accurately adhere to instructions in multi-turn dialogues.

MT-bench is a series of open-ended questions that evaluate a chatbot’s multi-turn conversational and instruction-following ability – two critical elements for human preference. MT-bench is also carefully constructed to differentiate chatbots based on their core capabilities, such as reasoning and math. In addition, we develop Chatbot Arena, a crowdsourced platform featuring anonymous battles between chatbots in real-world scenarios – Users engage in conversations with two chatbots at the same time and rate their responses based on personal preferences

Because these models are often trained with RLHF, they already exhibit strong human alignment. We call this approach “LLM-as-a-judge”.

We examine several potential limitations of the LLM-as-a-judge approach including position bias, verbosity bias, self-enhancement bias, and limited reasoning ability. We show that some of the biases are minor or can be mitigated.