Amazon Science proposes Ising-based aggregation for dependent LLM judges
Original titleWhen LLM judges agree, the right question is why. Shared prompts, model families, or training lineage can make a majority look stronger t...
AISummary
Amazon Science proposes dependence-aware aggregation using Ising models to account for shared prompts, model families, or training lineage among LLM judges. The post says this approach improves accuracy by 9–14% over weighted majority vote.
Source: Amazon Science · x.comPublished · added here