# From Rubrics to Reliable Scores: Evidence-Grounded Text Evaluation with LLM Judges

arXiv: https://arxiv.org/abs/2601.08654 · HTML: https://mlsanity.com/paper/2601.08654/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 34 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Builds on (most cited first, 23 of 23)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Large Language Models are not Fair Evaluators](https://mlsanity.com/paper/2305.17926/) (2023)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Prometheus: Inducing Fine-grained Evaluation Capability in Language Models](https://mlsanity.com/paper/2310.08491/) (2023)
- [SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization](https://mlsanity.com/paper/2111.09525/) (2021)
- [Asking and Answering Questions to Evaluate the Factual Consistency of Summaries](https://mlsanity.com/paper/2004.04228/) (2020)
- [JudgeLM: Fine-tuned Large Language Models are Scalable Judges](https://mlsanity.com/paper/2310.17631/) (2023)
- [Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models](https://mlsanity.com/paper/2404.18796/) (2024)
- [PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization](https://mlsanity.com/paper/2306.05087/) (2023)
- [LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks](https://mlsanity.com/paper/2406.18403/) (2024)
- [FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets](https://mlsanity.com/paper/2307.10928/) (2023)
- [Re-evaluating Evaluation in Text Summarization](https://mlsanity.com/paper/2010.07100/) (2020)
- [CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklists](https://mlsanity.com/paper/2403.18771/) (2024)
- [HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition](https://mlsanity.com/paper/2402.15754/) (2024)
- [Unleashing Large Language Models' Proficiency in Zero-shot Essay Scoring](https://mlsanity.com/paper/2404.04941/) (2024)
- [PEDANTS: Cheap but Effective and Interpretable Answer Equivalence](https://mlsanity.com/paper/2402.11161/) (2024)
- [Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments](https://mlsanity.com/paper/2406.11370/) (2024)
- [Bayesian Calibration of Win Rate Estimation with LLM Evaluators](https://mlsanity.com/paper/2411.04424/) (2024)
- [AutoSCORE: Enhancing Automated Scoring with Multi-Agent Large Language Models via Structured Component Recognition](https://mlsanity.com/paper/2509.21910/) (2025)

## Built on by (most cited first, 7 of 7)

- [Beyond Accuracy: Policy Invariance as a Reliability Test for LLM Safety Judges](https://mlsanity.com/paper/2605.06161/) (2026)
- [How Sensitive Are Safety Benchmarks to Judge Configuration Choices?](https://mlsanity.com/paper/2604.24074/) (2026)
- [HIPO: Instruction Hierarchy via Constrained Reinforcement Learning](https://mlsanity.com/paper/2603.16152/) (2026)
- [CARE: A Conformal Safety Layer for Medical Summarization](https://mlsanity.com/paper/2606.08969/) (2026)
- [Permutation-Consensus Listwise Judging for Robust Factuality Evaluation](https://mlsanity.com/paper/2603.20562/) (2026)
- [Graph-Structured Rubrics: Compiling Rubrics into Typed Evaluation Graphs for LLM Judges](https://mlsanity.com/paper/2608.12097/) (2026)
- [SoK: Grading Open-Text Legal Answers, from Law School Exams to Trustworthy Rubric Judges](https://mlsanity.com/paper/2610.19992/) (2026)

## Abstract (arXiv)

Rubric-based text evaluation increasingly relies on large language models (LLMs) as scalable judges, yet frozen black-box models can interpret the same criteria inconsistently, produce score attributions that are difficult to audit, and map judgments poorly onto human scoring scales. We define this challenge as criteria transfer: translating human rubric intent into a stable, auditable inference-time scoring protocol. We introduce Rulers, which locks a task-level rubric specification, executes it through structured, evidence-grounded judgments, and calibrates the resulting signals to human score boundaries. Across four rubric-governed benchmarks and multiple frozen backbone models, Rulers achieves stronger agreement with human scores in most evaluated settings, while better matching empirical score distributions and remaining more stable under semantically equivalent rubric perturbations. Calibration controls and component ablations show that these gains cannot be attributed to post-hoc alignment alone, but depend on the combination of fixed criteria, traceable evidence, and calibrated score interpretation. These findings suggest that reliable LLM judging requires faithfully operationalizing human evaluation standards rather than relying on prompt-level scoring alone. Our code is available at https://github.com/LabRAI/Rulers.git.