RubricRAG: Towards Interpretable and Reliable LLM Evaluation via Domain Knowledge Retrieval for Rubric Generation

• URL: http://arxiv.org/abs/2603.20882v1
• Date: Sat, 21 Mar 2026 17:10:14 GMT
• Title: RubricRAG: Towards Interpretable and Reliable LLM Evaluation via Domain Knowledge Retrieval for Rubric Generation
• Abstract summary: Large language models (LLMs) are increasingly evaluated and sometimes trained using automated graders that output scalar scores or preferences.<n>This lack of interpretability limits their usefulness for model development, dataset curation, and high-stakes deployment.<n>We introduce a simple strategy,RAG, which retrieves domain knowledge via rubrics at inference time from related queries.
• Score: 11.21565372620296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are increasingly evaluated and sometimes trained using automated graders such as LLM-as-judges that output scalar scores or preferences. While convenient, these approaches are often opaque: a single score rarely explains why an answer is good or bad, which requirements were missed, or how a system should be improved. This lack of interpretability limits their usefulness for model development, dataset curation, and high-stakes deployment. Query-specific rubric-based evaluation offers a more transparent alternative by decomposing quality into explicit, checkable criteria. However, manually designing high-quality, query-specific rubrics is labor-intensive and cognitively demanding and not feasible for deployment. While previous approaches have focused on generating intermediate rubrics for automated downstream evaluation, it is unclear if these rubrics are both interpretable and effective for human users. In this work, we investigate whether LLMs can generate useful, instance-specific rubrics as compared to human-authored rubrics, while also improving effectiveness for identifying good responses. Through our systematic study on two rubric benchmarks, and on multiple few-shot and post-training strategies, we find that off-the-shelf LLMs produce rubrics that are poorly aligned with human-authored ones. We introduce a simple strategy, RubricRAG, which retrieves domain knowledge via rubrics at inference time from related queries. We demonstrate that RubricRAG can generate more interpretable rubrics both for similarity to human-authored rubrics, and for improved downstream evaluation effectiveness. Our results highlight both the challenges and a promising approach of scalable, interpretable evaluation through automated rubric generation.

Related papers

• GenRubric: Self-Evolving Rubric Generation for Scalable LLM Evaluation [74.44235943118994]
  GenRubric is a self-evolving framework that improves rubric generation from unlabeled queries.<n>We implement this principle through reinforcement learning, combining a cross-rubric comprehensiveness signal with group-level and criterion-level rewards for rubric quality.<n> Experiments on human-annotated rubric benchmarks show that self-evolution improves the agreement between evaluations induced by generated rubrics and those induced by expert-written rubrics.
  arXiv  Detail & Related papers  (2026-08-30T15:39:39Z)
• Can LLMs Write Reliable Rubrics? A Meta-Evaluation for Experiment Reproduction [38.170020271877085]
  We present the first systematic meta-evaluation of LLM-generated rubrics for paper reproduction.<n>We reformulate rubrics into a checklist-style format and evaluate four generation settings across two backbone models.
  arXiv  Detail & Related papers  (2026-07-14T14:52:42Z)
• Optimizing In-Context Demonstrations for LLM-based Automated Grading [31.353360036776976]
  GUIDE (Grading Using Iteratively Designed Exemplars) is a framework that reframes exemplar selection and refinement as a boundary-focused optimization problem.<n>We show that GUIDE significantly outperforms standard retrieval baselines in experiments in physics, chemistry, and pedagogical content knowledge.
  arXiv  Detail & Related papers  (2026-02-28T04:52:38Z)
• Learning Query-Specific Rubrics from Human Preferences for DeepResearch Report Generation [80.12435680651488]
  We propose a pipeline to train human-preference-aligned query-specific rubric generators tailored for DeepResearch report generation.<n>We first construct a dataset of DeepResearch-style annotated queries with human preferences over paired reports, and train rubric generators via reinforcement learning.<n>We empirically show that our proposed rubric generators deliver more discriminative and better human-aligned supervision than existing rubric design strategies.
  arXiv  Detail & Related papers  (2026-02-03T15:09:56Z)
• CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [50.97588334916863]
  We develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward.<n>It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types.<n>We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier.
  arXiv  Detail & Related papers  (2025-08-05T17:55:24Z)
• Criteria-Based LLM Relevance Judgments [5.478764356647438]
  Large Language Models (LLMs) provide a scalable solution by generating relevance labels directly through prompting.<n>We propose the Multi-Criteria framework for LLM-based relevance judgments, decomposing the notion of relevance into multiple criteria.<n>Our results demonstrate that Multi-Criteria judgments enhance the system ranking/leaderboard performance.
  arXiv  Detail & Related papers  (2025-07-13T04:21:21Z)
• SCAN: Structured Capability Assessment and Navigation for LLMs [54.54085382131134]
  textbfSCAN (Structured Capability Assessment and Navigation) is a practical framework that enables detailed characterization of Large Language Models.<n>SCAN incorporates four key components:.<n>TaxBuilder, which extracts capability-indicating tags from queries to construct a hierarchical taxonomy;.<n>RealMix, a query synthesis and filtering mechanism that ensures sufficient evaluation data for each capability tag;.<n>A PC$2$-based (Pre-Comparison-derived Criteria) LLM-as-a-Judge approach achieves significantly higher accuracy compared to classic LLM-as-a-Judge method
  arXiv  Detail & Related papers  (2025-05-10T16:52:40Z)
• SedarEval: Automated Evaluation using Self-Adaptive Rubrics [4.97150240417381]
  We propose a new evaluation paradigm based on self-adaptive rubrics.<n>SedarEval consists of 1,000 meticulously crafted questions, each with its own self-adaptive rubric.<n>We train a specialized evaluator language model (evaluator LM) to supplant human graders.
  arXiv  Detail & Related papers  (2025-01-26T16:45:09Z)
• Towards Understanding the Robustness of LLM-based Evaluations under Perturbations [9.944512689015998]
  Large Language Models (LLMs) can serve as automatic evaluators for non-standardized metrics in summarization and dialog-based tasks.<n>We conduct experiments across multiple prompting strategies to examine how LLMs fare as quality evaluators when compared with human judgments.
  arXiv  Detail & Related papers  (2024-12-12T13:31:58Z)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [66.93260816493553]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios.<n>With a focus on factual accuracy, we propose three novel metrics: Completeness, Hallucination, and Irrelevance.<n> Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• Evaluating Human Alignment and Model Faithfulness of LLM Rationale [66.75309523854476]
  We study how well large language models (LLMs) explain their generations through rationales. We show that prompting-based methods are less "faithful" than attribution-based explanations.
  arXiv  Detail & Related papers  (2024-06-28T20:06:30Z)
• Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
  We propose a new evaluation method, SQC-Score. Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
  arXiv  Detail & Related papers  (2024-04-04T15:36:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.