A 1.88-million-article biomedical summarization dataset is released and quality-aware selection of training data based on abstract alignment outperforms random sampling on factuality metrics.
Finesure: Fine-grained summarization evaluation using llms
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EvoRubric is a single-policy RL method that co-evolves a reasoner and a rubric generator with multi-level verification to produce dynamic rewards for open-ended LLM alignment.
For binary LLM judge validation, Pearson's r, Spearman's ρ, Kendall's τ_b, phi, and Matthews correlation all equal a single number on non-degenerate data, Cohen's κ supplies the extra signal on label-rate drift, and a reporting checklist is provided.
citing papers explorer
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Less is More: Quality-Aware Training Data Selection for Scientific Summarization
A 1.88-million-article biomedical summarization dataset is released and quality-aware selection of training data based on abstract alignment outperforms random sampling on factuality metrics.
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EvoRubric: Self-Evolving Rubric-Driven RL for Open-Ended Generation
EvoRubric is a single-policy RL method that co-evolves a reasoner and a rubric generator with multi-level verification to produce dynamic rewards for open-ended LLM alignment.
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Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why
For binary LLM judge validation, Pearson's r, Spearman's ρ, Kendall's τ_b, phi, and Matthews correlation all equal a single number on non-degenerate data, Cohen's κ supplies the extra signal on label-rate drift, and a reporting checklist is provided.