Benchmarking Update: Baselines, MQM QA, WCAG 2.2 Testing, Ablations, and LLM Inference TCO

Context#

Benchmarking guidance has been expanded to cover consistent baselines for ML, structured bilingual QA, accessibility conformance with WCAG 2.2, rigorous ablation methodologies, and practical LLM inference TCO planning. This consolidation focuses on reliability, comparability, and actionable evaluation practices.

What’s new and why it matters#

• Baselines for ML experiments
• Establish a clear first point of comparison for all experiments.
• Acceptable options include non-ML references (e.g., rule-based systems) or simple statistical metrics.
• Why it matters: Ensures all subsequent improvements can be measured against a known starting point.

• MQM for bilingual QA and MT output evaluation
• Integrate MQM to assess translation quality, identify specific error patterns, and inform post-editing.
• Why it matters: Brings a consistent, error-focused framework to translation benchmarking.

• Accessibility benchmarking aligned with WCAG 2.2 and ACT Rules
• New success criteria at Level AA, including:
• 2.4.11 Focus Not Obscured (Minimum)
• 2.4.13 Focus Appearance
• ACT Rules formalize testing approaches and link outcomes directly to WCAG conformance: passing indicates no detected failures; failing indicates the related success criteria are not satisfied.
• Tooling vs human review: Automated checks are strong for technical issues (e.g., alt text, contrast), while human evaluation remains essential for context and meaning.
• Legal context: WCAG 2.2 is incorporated into US Section 508; ADA Title II often specifies earlier versions, but aligning to 2.2 strengthens compliance posture.
• Why it matters: Provides clearer, testable benchmarks, improves consistency, and reduces legal risk.

• Accessibility error landscape and priorities
• Six issues drive the vast majority of detectable failures, with low contrast text and missing alternative text among the most prevalent.
• ARIA usage correlates with more detected errors when misapplied.
• Sector trends: some segments (e.g., government, education, social media, technology, personal finance) perform better; others (e.g., sports, shopping/e-commerce, style & fashion) perform worse.
• Improvement remains slow year over year; focus on the common issues yields the highest return.
• Why it matters: Concentrating remediation on high-prevalence issues can measurably reduce failure rates.

• Ablation studies for model/component contribution analysis
• Core principle: isolate variables—change one component at a time while keeping all else constant.
• Structured approach: identify components, define hypotheses, design controlled variants, run experiments under identical conditions, and quantify impacts.
• Applicability spans domains (e.g., object detection modules) by holding shared parts fixed and varying the target module.
• Why it matters: Clarifies which elements truly drive performance, preventing over-attribution and guiding targeted improvements.

• LLM inference TCO benchmarking inputs
• Specify model size, GPU type (e.g., H100/H200), and VRAM per GPU.
• Define throughput metrics (Prefill TPS per GPU, Decode TPS per GPU) and a clear latency SLO.
• Why it matters: Enables apples-to-apples cost–performance planning and capacity sizing.

• Benchmarks for Japanese professional writing
• Reference to a standard style guide for translation that sets professional benchmarks for consistency and error prevention.
• Why it matters: Provides a quality baseline for Japanese language production and review.

• Model governance across the lifecycle
• Includes responsible candidate assessment and model inventory/metadata management.
• Why it matters: Keeps benchmarking and evaluation accountable, traceable, and aligned with business and ethical guardrails.

Practical checklist to stand up a robust benchmark plan#

• Define a baseline
• Choose a non-ML or simple heuristic/statistical baseline and record its metric as the comparison anchor.
• Scope quality frameworks by task
• For MT/bilingual QA: adopt MQM to classify errors and guide post-editing.
• For accessibility: target WCAG 2.2 Level A/AA with ACT-aligned tests; remember focus-related criteria (2.4.11, 2.4.13).
• Combine automated checks (contrast, alt text) with human review for semantics and UX.
• Plan ablation studies
• Freeze all but one component; vary that component; run under identical conditions; quantify contribution.
• Capture cost–performance inputs for LLM inference
• Record model size, GPU type and VRAM, throughput (Prefill/Decode TPS per GPU), and latency SLO.
• Apply style and domain standards where relevant
• For Japanese content, align to recognized professional writing/translation standards to reduce variability.
• Govern the process
• Maintain model inventory and metadata; assess candidates for feasibility, ethics, and business alignment.

Expected impact#

• More reliable comparisons through disciplined baselines and controlled ablations.
• Higher translation QA signal via MQM’s error taxonomy and post-editing guidance.
• Stronger accessibility conformance with WCAG 2.2 and ACT-linked testing, focusing remediation on the most prevalent failures.
• Clearer TCO/throughput–latency trade-offs for LLM inference planning.
• Improved consistency in Japanese writing and translation quality.

Next steps#

• Adopt the checklist above for upcoming experiments and audits.
• Prioritize remediation on common accessibility failures to gain immediate quality wins.
• Use ablation and baseline data to guide targeted model or component investments.
• Track TCO inputs alongside quality metrics to align performance with budget and SLOs.