AutoEval文本奖励模型：预测人类偏好比前沿LLM裁判准确8-10%
Chai团队搞了个AutoEval奖励模型，预测人类偏好比GPT-4之类还准8-10%，新模型刷榜前先看它评分，省得等人类投票。
AutoEval团队训练了一个奖励模型，专门应对偏好漂移、模型差异细微、平局投票以及长上下文依赖交互等挑战。评估采用时间留出法：用历史投票训练，测试后续发布的模型。早期评分与实时排名的排名相关性超过0.98。该文本奖励模型预测人类偏好的准确率比前沿LLM裁判高8-10%，可作为人类投票积累前的早期排行榜信号。
We train our reward model to handle challenges like preference shifts, increasingly subtle model differences, ambiguous tie votes, and longer and more context-dependent interactions. To evaluate it fairly, we use a temporal holdout: train on historical votes, and test on models released later. AutoEval’s early scores achieved >0.98 ranking correlation with the live scores that followed. Our text reward model also predicts human preferences 8-10% more accurately than frontier LLM judges. This is what makes AutoEval useful as an early leaderboard signal while human votes are still accumulating. 💬 1 🔄 1 ❤️ 3 👀 751 📊 2 ⚡