arXiv AI
ExecRubrics: Executable Tool-Augmented Rubrics for Verifiable and Efficient Long-Form Evaluation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
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.
arXiv Computation and Language
Aug 27
Retrieval-Augmented Agentic Rubric Generation for Reliable Medical Response Evaluation
The paper introduces a retrieval‑augmented multi‑agent framework that automatically generates instance‑specific evaluation rubrics for medical language models. By retrieving authoritative medical evidence, decomposing it into atomic facts, and combining these with user interaction constraints, the system produces fine‑grained criteria that outperform GPT‑4o on HealthBench and LLMEval‑Med. The generated rubrics also guide response refinement, improving medical LLM output quality by 9.2%.
arXiv Computation and Language
Sep 10
From Rubrics to Reliable Scores: Evidence-Grounded Text Evaluation with LLM Judges
arXiv:2601.08654v3 Announce Type: replace Abstract: Rubric-based text evaluation increasingly relies on large language models (LLMs) as scalable judges, yet frozen black-box models can interpret the...
arXiv Computation and Language
Sep 1
GenRubric: Self-Evolving Rubric Generation for Scalable LLM Evaluation
arXiv:2608.29856v1 Announce Type: new Abstract: Large language models are increasingly used as scalable evaluators for open-ended tasks. However, many LLM judges derive query-specific criteria during...
arXiv AI
Aug 13
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.
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.