AI BriefingKakao Introduces LLM as a Judge for CodeBuddy Performance Evaluation… Proposes Bias Mitigation Strategies
Key point
Kakao CodeBuddy utilizes LLM as a Judge for performance evaluation, proposing methods to enhance evaluation reliability by improving prompt structure and mitigating position bias.
Details
Kakao's AI coding assistant CodeBuddy has introduced the LLM as a Judge technique for complex PR code reviews and performance evaluations. During this process, issues inherent to LLM evaluators, such as self-bias, verbosity bias, and position bias, were analyzed. In particular, it was confirmed that failing to clearly distinguish between System Message and User Message in evaluation prompts results in a high tie rate of over 80%, reducing discriminative power. Conversely, clearly distinguishing these messages ensured high discriminative power. Additionally, in code improvement (Improve) tasks, position bias was notably prominent, with win rates varying by up to 28% depending on the order of candidate responses. Accordingly, Kakao proposed strategies to enhance evaluation reliability, including utilizing multiple evaluators, averaging results through repeated evaluations, and clarifying evaluation criteria within prompts.
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