Accelerating Unbiased LLM Evaluation via Synthetic Feedback
This work addresses the problem of efficient and unbiased evaluation of large language models for developers and researchers in the natural language processing community, providing an incremental solution to reduce the need for human annotations.
The authors tackled the problem of costly human evaluations for large language models by proposing a framework that integrates human and synthetic feedback, resulting in a reduction of human annotations by up to 24.8%. This reduction in human annotations can accelerate the evaluation process.
When developing new large language models (LLMs), a key step is evaluating their final performance, often by computing the win-rate against a reference model based on external feedback. Human feedback is the gold standard, particularly for capturing nuanced qualities like coherence, readability, and alignment with human expectations. However, human evaluations are costly -- even for large tech companies -- and when conducted with active users, they may negatively impact user experience. A promising alternative is synthetic feedback, where evaluations are conducted by other large language models, including reward models. While this eliminates the need for costly human annotations, it introduces biases that may distort the evaluation process. In this work, we propose a statistically principled framework that integrates human and synthetic feedback to reduce reliance on human annotations while maintaining unbiased win-rate calculations. Our experiments demonstrate a reduction in human annotations by up to 12.2% with an off-the-shelf synthetic evaluator and up to 24.8% with a finetuned variant. Apart from being generalizable, scalable, and free of hyper-parameter tuning, our method offers predictable annotation savings, which can be estimated based on data-dependent characteristics.