Researchers introduce AGORA method for improving LLM judge reliability
A new approach called AGORA aims to improve the reliability of large language model (LLM) judges by accounting for their varying strengths. Unlike current methods that treat all judges equally, AGORA uses item-response theory to weigh each judge’s performance individually, learning from all evaluated items without needing human-labeled validation sets.
Key points
- AGORA uses item-response theory to weigh LLM judges by reliability, not just raw scores
- Outperforms Dawid-Skene on benchmarks, especially with small evaluation sets under 100 items
- Predicts when uneven judge pools benefit from trust weighting without human labels
The method outperforms existing techniques like Dawid-Skene on three benchmarks, especially when evaluation sets are small—fewer than a few hundred items. AGORA also predicts when uneven judge pools benefit from trust weighting and aligns learned trust scores with human validity. The paper, posted on arXiv, suggests AGORA could make LLM-based evaluation more accurate and scalable in scenarios where human oversight is limited.
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