Accelerating Unbiased LLM Evaluation via Synthetic Feedback

2025-02-14

AI Summary

Evaluating large language models (LLMs) via costly human feedback suffers from low efficiency, poor user experience, and limited scalability. Method: This paper proposes an unbiased statistical evaluation framework that synergistically integrates human and synthetic feedback. It introduces a novel, parameter-free, predictability-aware hybrid feedback calibration mechanism, combining statistical bias correction with synthetic feedback integration to ensure unbiased win-rate estimation while minimizing human annotation requirements. Contribution/Results: On standard LLM evaluation benchmarks, the out-of-the-box and fine-tuned variants of our framework reduce human annotation costs by 12.2% and 24.8%, respectively, without compromising evaluation fairness or accuracy. The framework is general-purpose and scalable, enabling large-scale automated deployment across diverse LLM assessment scenarios.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• Reducing human evaluation costs in LLM development
• Mitigating biases from synthetic LLM feedback
• Maintaining unbiased win-rate calculations with less human input

Innovation

• Integrates human and synthetic feedback
• Reduces reliance on human annotations
• Maintains unbiased win-rate calculations