Ground truth is a process, not a dataset

Summary

Evaluating search-augmented AI research reports requires verifying complex, multi-source claims that depend heavily on surrounding context. Unassisted PhD-level domain specialists tasked with establishing static ground-truth labels achieved only 60.8% accuracy on a hidden test set during controlled testing. To resolve benchmark errors on cognitively demanding tasks, Amazon AGI introduced the audit-then-score evaluation protocol alongside DeepFact-Bench and the DeepFact-Eval verification system. Under this protocol, model disagreements serve as challenges requiring concrete counterevidence and written rationales, enabling human auditors to evaluate competing claims and update benchmark ground truth prior to scoring. Operating under this iterative workflow, expert test accuracy rose to 90.9% across four rounds, and DeepFact-Eval reached 83.4% accuracy with GPT-4.1.

Takeaways

1. 1

   Unassisted PhD-level specialists evaluating AI research claims from scratch achieved only 60.8% accuracy on a hidden set of known answers.

2. 2

   Transitioning domain experts from one-shot labelers to auditors comparing competing rationales increased hidden test set accuracy from 60.8% to 90.9% across four rounds.

3. 3

   DeepFact-Eval using GPT-4.1 achieved 83.4% accuracy on DeepFact-Bench, outperforming traditional fact checkers at 58.5% and a prior deep-research system at 69.1%.