Should we benchmark conceptual capabilities using judgment prediction tasks?

Benchmarking how language models reason about subjective conceptual questions is tricky: legitimate disagreement between humans obscures whether the AI is actually improving at reasoning or just matching different philosophical tastes. The AI Alignment Forum explores a potential solution called judgment prediction, where you ask the model to predict what a specific expert would judge on contested questions, rather than seeking objective truth. This isolates measurement of actual reasoning capability from the model's own priors. But it has practical downsides—judges' answers can be noisy, the model might already know the judge's published views, and AI systems often struggle to authentically adopt an expert's worldview without falling into superficial mimicry. Still, the author concludes that a simpler version is worth doing: run conceptual benchmarks on subjective questions while providing the model with background context on the judge's thinking.

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