arXiv AI
Jul 23
Self-Preference Bias in Rubric-Based Evaluation of Large Language Models
arXiv:2604. 06996v2 Announce Type: replace-cross Abstract: LLM-as-a-judge has become the de facto approach for evaluating LLM outputs.
Graph-Structured Rubrics: Compiling Rubrics into Typed Evaluation Graphs for LLM Judges
arXiv:2608. 12097v1 Announce Type: new Abstract: Rubric-based evaluators commonly treat rubrics as prompt context or flat criteria: they specify what to judge but leave criterion composition implicit, even when natural-language rules state it.
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arXiv AI
Jun 24
Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules
arXiv:2508. 10971v2 Announce Type: replace-cross Abstract: Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret due to their inherent complexity and the idiosyncratic labeling conventions of individual KGs.
Hugging Face Trending Papers
Jul 2
Many Voices, One Reward: Multi-Role Rubric Generation for LLM Judging and Reward Modeling
Reliable reward and preference signals are critical for evaluating and optimizing large language models on open-ended tasks. Rubric-based judges offer a transparent way to decompose such judgments into explicit evaluation criteria, but existing annotation-free rubric generators typically rely on a single generic evaluator.
arXiv Machine Learning
Jul 3
Many Voices, One Reward: Multi-Role Rubric Generation for LLM Judging and Reward Modeling
arXiv:2607. 01830v1 Announce Type: new Abstract: Reliable reward and preference signals are critical for evaluating and optimizing large language models on open-ended tasks.
Hugging Face Trending Papers
Jul 14
Can LLMs Write Reliable Rubrics? A Meta-Evaluation for Experiment Reproduction
Rubric-based evaluation is a promising approach for assessing open-ended outputs from LLM-based research agents, particularly in paper reproduction, where direct paper-to-repository comparison is prone to hallucination. However, constructing paper-specific rubrics requires substantial expert effort, limiting the scalability of benchmarks such as PaperBench.
arXiv AI
1d ago
Mitigating Rubric Interference in LLM Judges via On-Policy Self-Distillation
arXiv:2608. 14684v1 Announce Type: cross Abstract: LLM judges increasingly evaluate responses against fine-grained rubric checklists.
arXiv AI
Jun 3
JudgmentBench: Comparing Rubric and Preference Evaluation for Quality Assessment
arXiv:2605. 25240v2 Announce Type: replace-cross Abstract: Two methodologies dominate current practices of benchmarking: rubric-based scoring evaluates items against predefined criteria, whereas comparative judgment elicits pairwise preferences between outputs.
arXiv AI
Jun 9
Reliable to Expressive: A Curriculum for Rubric-Following Safety Judges
arXiv:2606. 09165v1 Announce Type: new Abstract: Safety judges are increasingly deployed to evaluate model outputs against evolving criteria, yet recent meta-evaluation work shows they remain brittle under prompt and rubric variation, with false negative-rate swings of up to 0.
arXiv AI
Jun 3
GTBench: A Curriculum-Grounded Benchmark for Evaluating LLMs as Mathematical Research Assistants in Graph Theory
arXiv:2606. 03144v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as self-study assistants in technical disciplines, yet their reliability as mathematical reasoning assistants remains poorly understood.
arXiv AI
Jun 26
Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
arXiv:2606. 27226v1 Announce Type: new Abstract: Evaluating LLM outputs remains a major bottleneck in NLP: human evaluation is expensive and slow, lexical metrics correlate poorly with human judgments on open-ended generation, and holistic LLM judges often produce opaque scores that are hard to debug.
arXiv AI
5d ago
Unified Multi-Dimensional Benchmark for Complex Graph Reasoning in Large Language Models
arXiv:2608. 12391v1 Announce Type: cross Abstract: Graph reasoning provides a promising testbed for evaluating the reasoning ability of large language models (LLMs), as graph instances can be programmatically generated, structurally controlled, and naturally scaled to long-input settings.
Hugging Face Trending Papers
Jun 25
Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
Evaluating LLM outputs remains a major bottleneck in NLP: human evaluation is expensive and slow, lexical metrics correlate poorly with human judgments on open-ended generation, and holistic LLM judges often produce opaque scores that are hard to debug. We propose BINEVAL, a framework that decomposes evaluation criteria into atomic binary questions and aggregates the resulting verdicts into interpretable, multi-dimensional scores.