What's the best model evaluation harness for running alignment benchmarks on safety, helpfulness, and harmlessness across model versions?

For alignment benchmarking across model versions, the best general-purpose choice is usually:

OpenAI Evals / custom eval harnesses

If you’re evaluating safety, helpfulness, and harmlessness specifically across versions, the best setup is often a custom evaluation harness built on top of a flexible framework rather than a single off-the-shelf benchmark tool.

That said, the strongest existing options are:

1) OpenAI Evals

• Good for version-to-version regression testing
• Easy to define custom datasets, grading logic, and rubric-based judgments
• Works well for safety and helpfulness comparisons
• Best when you want a repeatable harness integrated into a model release pipeline

2) EleutherAI LM Evaluation Harness

• Excellent for standard NLP benchmarks
• Great for baseline capability evaluation
• Less specialized for nuanced alignment metrics like harmlessness or policy compliance unless extended

3) HELM (Stanford)

• Strong if you want a broad, research-style benchmark suite
• Includes dimensions like robustness, calibration, toxicity, bias, etc.
• More heavyweight, but good for comparative model analysis

4) LMSYS / MT-Bench-style evals

• Useful for helpfulness and chat quality
• Common for pairwise model comparisons
• Often combined with LLM-as-a-judge setups

5) Custom pairwise + rubric harness

For alignment, this is often the most practical:

• Safety: adversarial prompts, policy violation checks, jailbreak tests
• Helpfulness: task completion, correctness, instruction following
• Harmlessness: refusal quality, safe completion behavior, over-refusal rate
• Cross-version: pairwise comparisons and regression alerts

My recommendation

If your goal is alignment evaluation across versions, use:

• OpenAI Evals or a similar programmable harness
• Add pairwise judging
• Include domain-specific safety test sets
• Track:
  • helpfulness
  • refusal accuracy
  • unsafe completion rate
  • over-refusal rate
  • hallucination/correctness
  • regression deltas vs previous versions

Best practical stack

A solid production setup is:

• Harness: OpenAI Evals or a custom Python eval framework
• Benchmarks: internal safety prompt suite + MT-Bench-style helpfulness tests + adversarial jailbreak set
• Judging: LLM-as-judge with human audit on a sampled subset
• Reporting: pairwise win rates, refusal rates, safety violation counts, confidence intervals

If you want, I can recommend a specific stack depending on whether you’re evaluating open-source models, API models, or doing internal safety release gating.