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Human Feedback and Eval Paper Explorer

A focused feed for RLHF, preference data, rater protocols, agent evaluation, and LLM-as-judge research. Every paper includes structured metadata for quick triage.

Total papers: 1 Search mode: keyword Shortlist (0) RSS

Featured Papers

Popular high-signal papers with direct links to full protocol pages.

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Need human evaluators for your benchmark or preference study? OpenTrain sources pre-vetted domain experts into your annotation pipeline.

Match reason: Matched by broad semantic/index fallback.

Score: 26% Sparse protocol signal Freshness: Cold Status: Ready
General
  • In our benchmark, LLMs compete in generating sets of analogous statements and rate each other's sets on their own understandings of factual correctness, beauty, intelligence, distinctness, length, and structural diversity.
  • GPQA Diamond -- difficult multiple-choice questions written by human experts -- could not be more different in method, yet the two benchmarks correlate at Pearson r = 0.97, 95% CI [0.92, 0.99]; no leakage could be found.
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