Benchmark Foundations: Baselines, Ablations, MQM for Bilingual QA, LLM TCO, and WCAG 2.2 Metrics

Context#

Recent updates expanded benchmarking and evaluation guidance across multiple areas, emphasizing strong baselines, disciplined experimentation, bilingual QA quality measurement, cost benchmarking for inference, and accessibility conformance and metrics.

What’s new and relevant to benchmarking#

• Baselines
• Establish a baseline as the first point of comparison for all experiments (e.g., simple rule-based or statistical alternatives).
• Experiment design and ablations
• Run ablation studies by isolating a single component at a time and holding all other variables constant to measure its contribution.
• Apply this pattern across domains (e.g., vary one module while keeping feature extraction and classification constant in object detection).
• Principles: isolate variables, use consistent conditions, and compare against the baseline.
• Data hygiene for evaluation
• Maintain strict separation of training, development (validation), and evaluation (test) sets, and keep the evaluation set held out.
• Bilingual QA and MQM
• Integrate MQM into bilingual QA workflows to evaluate machine translation output quality, identify error patterns, and inform post-editing.
• LLM inference TCO benchmarking
• Capture key drivers when modeling total cost: model size and GPU type/VRAM; throughput (prefill/decoder TPS per GPU); and latency SLOs.
• Model governance alignment
• Implement governance from candidate assessment through inventory/metadata and beyond to ensure responsible, auditable lifecycle management.
• Accessibility conformance and metrics (WCAG / ACT Rules)
• WCAG 2.2 adds new success criteria under the POUR principles; it is incorporated into certain US government standards (e.g., Section 508).
• ACT Rules provide consistent, comparable testing; a failed ACT Rule implies the corresponding WCAG success criteria are not satisfied.
• Automated tools are strong for technical checks (e.g., contrast, alt text), while human evaluation remains necessary for context and meaning.
• Trends and issues:
• The most prevalent accessibility problems are concentrated in a small set of recurring issues (approximately 96% of detected errors), including low contrast text and missing alternative text for images.
• Pages with ARIA tend to correlate with more detectable errors (e.g., often around double).
• Sector differences persist, with some sectors typically performing better and others worse.
• Year-over-year improvements are modest; focusing on the common issues yields the highest ROI.

Practical benchmark playbooks#

• Establish the baseline
• Document the baseline approach and metric(s) as the canonical comparison point.
• Design ablations
• Change one component at a time; keep data, training procedures, and evaluation constant; report deltas relative to the baseline.
• Protect evaluation integrity
• Freeze the evaluation set; use development data for iteration; report final metrics only on the held-out set.
• Bilingual QA with MQM
• Define error categories and severities; sample outputs for human review; use MQM to identify patterns; loop insights back into post-editing and training.
• LLM inference TCO targets
• Record assumptions for model/GPU/VRAM; measure prefill and decode throughput per accelerator; test against latency SLOs; compare options on cost per token under SLO.
• Accessibility benchmarking
• Align tests to WCAG 2.2. For Level AA, include items such as focus visibility and target size where applicable.
• Use ACT Rules to standardize checks; treat rule failures as non-conformant; combine automated scans with human review for semantics and UX.
• Prioritize remediation of recurring high-impact issues like contrast and alternative text; monitor ARIA usage carefully.

Repository changes snapshot (high level)#

• Numerous knowledge artifacts were added or updated, expanding coverage across:
• Japanese linguistics (including keigo, dialectal variants, and bilingual indexing strategies).
• Practitioner accounting and tax workflows with bilingual QA sampling and privacy-by-design considerations.
• Corporate operations, procurement, vendor management, and regulatory crosswalks.
• Media, design, and product design classifications and operational knowledge.
• Project lifecycle governance, gates, owners, SLAs, and rollback guidance.
• Indexing and metadata were refreshed to register these additions and assignments.
• A minor CI credential configuration update was performed with balanced insertions and deletions.

How to apply this immediately#

• Define your baseline and lock your evaluation set before new experiments.
• Plan a minimal ablation matrix to quantify contributions of key components.
• For bilingual workflows, adopt MQM sampling to drive targeted fixes.
• For inference cost benchmarks, measure throughput and latency under realistic SLOs.
• Run an accessibility sweep aligned to WCAG 2.2 and ACT Rules; prioritize the common high-impact issues first.

References within the updated knowledge#

• Baselines for ML experiments
• MQM integration for bilingual QA
• LLM inference TCO drivers
• Model governance stages
• WCAG 2.2 additions, ACT Rules usage, and observed web accessibility trends
• Ablation study principles and evaluation data splits