Four self-stigma personas identified via LPA on 1,174 Reddit users; persona-conditioned LLMs achieve targeted shifts but experts prefer generic empathy baselines.
LLM -rubric: A multidimensional, calibrated approach to automated evaluation of natural language texts
11 Pith papers cite this work, alongside 33 external citations. Polarity classification is still indexing.
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2026 11representative citing papers
AsymmetryZero operationalizes expert preferences as stable evaluation contracts for semantic evals, with a study showing 75.9-89.6% criterion agreement between frontier and compact model juries at 4-5% of the cost.
ManyIH and ManyIH-Bench address instruction conflicts in LLM agents with up to 12 privilege levels across 853 tasks, revealing frontier models achieve only ~40% accuracy.
Experiments across code LLMs show no-review collapses fastest, human-gated filters slow collapse, and AI self-gates lose effect over time, degenerating to ungated self-training under self-confirming acceptance as proven via gated distributional reweighting and spectral analysis.
Skill-RM unifies heterogeneous reward criteria by modeling reward computation as dynamic execution of a reusable Reward-Evaluation Skill within an agent framework.
LP-Eval is a new expert-co-designed rubric and annotated dataset showing that LLMs mostly produce well-formed legal propositions from EU court decisions, with higher expert-rated quality for established cases and improved LLM-as-judge alignment when using the rubric.
Terminus-4B, a Qwen3-4B post-trained with SFT and rubric-based GRPO, matches or beats frontier models as a terminal-execution subagent while cutting main-agent tokens ~30% on SWE-Bench-style tasks.
On binary verdicts, Pearson, Spearman, Kendall's tau-b, phi, and the Matthews correlation are a single statistic, so most multi-metric agreement reports repeat one number under different names.
Case studies with blind UK residents and people from Kerala and Tamil Nadu demonstrate that community input at the systematization stage produces culturally grounded definitions of appropriateness for text-to-image model outputs.
The paper proposes and compares approaches for assessing interactive visualization abilities by linking them to existing literacy concepts and assessments.
The paper frames rubrics as a recurring structured-criteria approach that decomposes holistic judgments at evaluative, training, and intrinsic levels in LLM research.
citing papers explorer
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Self-Stigma Is Not a Monolith, but Generic Empathy Is: Persona-Conditioned LLM Support for People Who Use Drugs
Four self-stigma personas identified via LPA on 1,174 Reddit users; persona-conditioned LLMs achieve targeted shifts but experts prefer generic empathy baselines.
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AsymmetryZero: A Framework for Operationalizing Human Expert Preferences as Semantic Evals
AsymmetryZero operationalizes expert preferences as stable evaluation contracts for semantic evals, with a study showing 75.9-89.6% criterion agreement between frontier and compact model juries at 4-5% of the cost.
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Many-Tier Instruction Hierarchy in LLM Agents
ManyIH and ManyIH-Bench address instruction conflicts in LLM agents with up to 12 privilege levels across 853 tasks, revealing frontier models achieve only ~40% accuracy.
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When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs
Experiments across code LLMs show no-review collapses fastest, human-gated filters slow collapse, and AI self-gates lose effect over time, degenerating to ungated self-training under self-confirming acceptance as proven via gated distributional reweighting and spectral analysis.
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Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill
Skill-RM unifies heterogeneous reward criteria by modeling reward computation as dynamic execution of a reusable Reward-Evaluation Skill within an agent framework.
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LP-Eval: Rubric and Dataset for Measuring the Quality of Legal Proposition Generation
LP-Eval is a new expert-co-designed rubric and annotated dataset showing that LLMs mostly produce well-formed legal propositions from EU court decisions, with higher expert-rated quality for established cases and improved LLM-as-judge alignment when using the rubric.
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Terminus-4B: Can a Smaller Model Replace Frontier LLMs at Agentic Execution Tasks?
Terminus-4B, a Qwen3-4B post-trained with SFT and rubric-based GRPO, matches or beats frontier models as a terminal-execution subagent while cutting main-agent tokens ~30% on SWE-Bench-style tasks.
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Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why
On binary verdicts, Pearson, Spearman, Kendall's tau-b, phi, and the Matthews correlation are a single statistic, so most multi-metric agreement reports repeat one number under different names.
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Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics
Case studies with blind UK residents and people from Kerala and Tamil Nadu demonstrate that community input at the systematization stage produces culturally grounded definitions of appropriateness for text-to-image model outputs.
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Towards Measuring Interactive Visualization Abilities: Connecting With Existing Literacies and Assessments
The paper proposes and compares approaches for assessing interactive visualization abilities by linking them to existing literacy concepts and assessments.
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From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscape
The paper frames rubrics as a recurring structured-criteria approach that decomposes holistic judgments at evaluative, training, and intrinsic levels in LLM research.