Expert-Grounded Evaluation Taxonomy

Updated 22 December 2025

Expert-grounded evaluation taxonomy is a framework that defines and validates evaluation criteria through expert insights and systematic annotation protocols.
It employs expert review and adjudication, ensuring high inter-annotator reliability with metrics like Cohen’s κ and Krippendorff’s α.
The taxonomy is applied across diverse fields such as clinical medicine, finance, and language learning, enabling reproducible and actionable system assessments.

An expert-grounded evaluation taxonomy is a framework in which the criteria, structure, and operationalization of system or artifact evaluation are explicitly defined, validated, and applied by domain experts—often in conjunction with large-scale annotation protocols and standardized metrics. This approach ensures that taxonomies and benchmarks for system assessment are scientifically rigorous, reproducible, and operationally relevant, addressing both the theoretical and practical demands of high-stakes domains ranging from language learning and clinical medicine to software engineering and finance.

1. Theoretical Foundations and Rationale

Expert-grounded evaluation taxonomies are predicated on the need for reliable, actionable, and credible assessments. Purely algorithmic or crowd-sourced taxonomies often suffer from coverage gaps, category ambiguity, overlapping definitions, and poor alignment with professional practice. By grounding taxonomy construction and evaluation in expert judgment, these frameworks deliver:

Domain validity: All criteria, rubrics, and error classes reflect the language, structure, and priorities of specialists (e.g., clinicians, financial analysts, linguists).
Reproducibility: Systematic annotation, scoring, and validation protocols facilitate inter-annotator agreement and transferability to unseen data.
Actionability: Outputs can be directly used for system refinement, policy compliance, or end-user guidance.

Motivations for adopting this paradigm include overcoming the inconsistencies and unreliable feedback observed in earlier, non-expert-grounded taxonomies (Zou et al., 17 Feb 2025), increasing the scientific comparability of evaluation results (Belz et al., 26 Sep 2025), and providing trustworthy diagnostics in expert-level reporting tasks (Han et al., 19 Dec 2025).

2. Taxonomy Criteria and Formalization

A hallmark of expert-grounded evaluation taxonomies is the explicit definition of evaluation criteria—often operationalized as multidimensional metrics or rubrics validated by domain experts. Typical axis definitions include:

Criterion	Formalization	Purpose
Exclusivity	$\text{Exclusivity}(F) = \frac{1}{\|D\|} \sum_{x \in D} \begin{cases} 1 - \frac{\mathrm{Overlap}(x)-1}{k-1} & \mathrm{Overlap}(x)>0 \ 0 & \mathrm{Overlap}(x)=0 \end{cases}$	Ensures each instance receives a unique label (Zou et al., 17 Feb 2025)
Coverage	$\text{Coverage}(F) = \frac{\|U\|}{\|D\|}$	Assesses fraction of instances fitting known categories
Balance	$\text{Balance}(F) = \frac{\sum_{i=1}^m -P_i \log P_i}{\log m}$ where $P_i$ is class empirical distribution	Detects skewed/overfit taxonomy structures
Usability	Macro and micro F $_1$ (classification) or inter-annotator agreement (e.g., Cohen's $\kappa$ )	Measures practical effectiveness for models and humans

Elevated formalisms are also found in document-level scientific report evaluation, where each high-level dimension (e.g., Evidence Validity, Ethics Compliance) is refined to multi-level subcriteria and operationalized via fine-grained coverage/quality ratings (Han et al., 19 Dec 2025).

3. Expert Annotation, Adjudication, and Reliability

Annotation under expert-grounded frameworks typically follows a multi-stage pipeline:

LLM Pre-Annotation: Automated pre-labeling of instances or responses via hierarchical, expert-tuned prompts (Zou et al., 17 Feb 2025).
Expert Review and Correction: Iterative refinement by domain experts, correcting systematic misclassifications and calibrating ambiguous cases.
Blinded Independent Adjudication: Multiple experts conduct independent labeling; conflicts are solved via consensus or third-party adjudication.
Inter-Annotator Reliability Quantification: Agreement is measured via metrics such as Cohen’s $\kappa$ , Krippendorff’s $\alpha$ , or ICC, with values $\kappa \approx 0.71 - 0.80$ regarded as strong consistency (Zou et al., 17 Feb 2025, Han et al., 19 Dec 2025, Li et al., 10 Jun 2025).

This process ensures taxonomies and benchmarks maintain both internal consistency and external validity, and are robust against interpretive drift.

4. Quantitative Metrics and Statistical Evaluation

Expert-grounded frameworks emphasize rigorous, multidimensional scoring:

Task-level metrics: For classification, the exclusive use of normalized overlap, entropy-derived balance, and F $_1$ -based usability (Zou et al., 17 Feb 2025).
Quadrant or subtask partitioning: In dynamic expert-agent benchmarks, task taxonomies delineate operational complexity (simple/complex) and reasoning type (retrieval/prediction), with scoring per quadrant highlighting real-world capability boundaries (Guo et al., 29 Nov 2025, Hu et al., 16 Sep 2025).
Hierarchical aggregation: In extensive rubrics (e.g., 130-point DEER benchmark), subscores are recursively averaged across elements, subcriteria, and high-level dimensions (Han et al., 19 Dec 2025).
Inter-annotator agreement: Reliability scores such as Krippendorff’s $\alpha \geq 0.7$ for ordinal scales confirm practical reliability for deployment (Li et al., 10 Jun 2025).

Results are compared using standard hypothesis tests (paired $t$ -tests, signed-rank) to confirm significant differences, and performance gaps across taxonomies are visualized via frequency histograms and entropy plots (Zou et al., 17 Feb 2025).

5. Practitioner Workflows and Dataset Construction

A core function of expert-grounded taxonomies is guiding reproducible, high-fidelity benchmark and dataset creation:

Corpus selection and preprocessing: Sampling is stratified to isolate relevant unit instances (e.g., single grammatical errors, financial queries, radiology findings) (Zou et al., 17 Feb 2025, Hu et al., 16 Sep 2025, Jiang et al., 22 May 2025).
Annotation guidelines: Exhaustive, rubric-based instructions and exemplars are distributed among annotators to ensure unambiguous criteria application (Han et al., 19 Dec 2025).
Quality assurance: Multiple rounds of blind annotation, expert adjudication, and cross-source validation guarantee label fidelity (Hu et al., 16 Sep 2025).
Scoring: Benchmarks integrate both objective rubrics (binary or scale-based) and supporting rationales or span-level citations for error analysis (Li et al., 10 Jun 2025, Han et al., 19 Dec 2025).

Empirical reliability is further assured by reporting extensive agreement metrics and explicit scoring aggregation formulas.

6. Applications, Transferability, and Best Practices

Expert-grounded evaluation taxonomies have demonstrated broad applicability across domains:

Language learning: Syntax/morphology/vocabulary error taxonomies validated on high-quality, multi-annotator datasets (Zou et al., 17 Feb 2025).
Finance and agent workflows: Fine-grained operational taxonomies (retrieval vs. prediction, simple vs. complex) underpin robust and continually updated agent benchmarks (Guo et al., 29 Nov 2025, Hu et al., 16 Sep 2025).
Clinical medicine: Multi-attribute templates precisely mirror radiologist and physician reasoning, yielding interpretable, granular heatmaps for clinical report assessment (Jiang et al., 22 May 2025, Chen et al., 26 Sep 2025).
High-stakes expert reporting: Deep research benchmarks assess holistic report quality via multi-level, guidance-augmented checklists and document-wide fact-checking (Han et al., 19 Dec 2025).

Key best practices distilled across exemplars include:

Systematic partitioning of taxonomies for tractable and interpretable evaluation (Zhang et al., 2 Apr 2025).
Continuous expert oversight and rubric refinement to accommodate evolving practice standards (Han et al., 19 Dec 2025).
Standardized prompt structures and annotation templates to achieve invariance across annotators and instances.
Public release of code, datasets, and evaluation rubrics to foster rapid adoption and independent validation.

Practitioners are advised to rigorously define, annotate, and deploy evaluation criteria aligned with domain expertise, to report reliability and agreement statistics, and to use multi-dimensional and coverage/quality scores for comprehensive system assessment.

In sum, expert-grounded evaluation taxonomies enable multidimensional, robust, and actionable assessments that are essential for the development, benchmarking, and continuous refinement of systems in technically demanding domains. Through their reliance on explicit expert validation, rigorous annotation, and cross-validated quantitative metrics, they set a reproducible standard for empirical evaluation across the research landscape (Zou et al., 17 Feb 2025, Guo et al., 29 Nov 2025, Hu et al., 16 Sep 2025, Jiang et al., 22 May 2025, Han et al., 19 Dec 2025, Chen et al., 26 Sep 2025, Li et al., 10 Jun 2025, Zhang et al., 2 Apr 2025, Belz et al., 26 Sep 2025).