Eval Methods

Eval Methods # 

Eval_Methods/
├── AI_Evals/                    # Alignment-focused evals (e.g., OpenAI evals)
│   ├── OpenAI_Evals/
│   ├── Benchmark_Suites/
│   └── Eval_Metrics/
├── Human-in-the-Loop/          # Evaluation strategies w/ annotators
│   ├── Labeler-Guides/
│   └── HITL-Pipelines.md
├── Eval Frameworks/            # Tools: helm, trl.eval, chat-arena
└── Monitoring_vs_Eval.md       # Clarify ops-vs-research boundary

AI_Evals/ — Evaluation Content Focused on Alignment #

  • Goal: Evaluate how well AI models behave according to human preferences and task goals.

  • OpenAI_Evals/
    For evaluating models with OpenAI’s evals framework — includes preference rankings, math prompts, multi-turn responses, and tool use evals.

  • Benchmark_Suites/
    Curated sets of standard benchmark tasks like:

    • TruthfulQA (factual alignment)
    • MMLU (multitask understanding)
    • BIG-Bench (general reasoning)
    • MT-Bench / Arena-Hard (comparative LLM evals)

  • Eval_Metrics/
    Standard and emerging metrics to quantify:

    • Helpfulness
    • Harmlessness
    • Coherence
    • Factuality
    • Preference alignment

  • Use when: you want to compare models quantitatively or analyze behavioral drift across training versions.

Human-in-the-Loop/ — Crowdsourced or Expert Human Judgments #

  • Goal: Structure manual evaluation workflows using human labelers or expert annotators.

  • Labeler-Guides/
    Guidelines and templates for human evaluators:

    • Rating rubrics
    • Examples of “good vs bad” outputs
    • Ethical and fairness considerations

  • HITL-Pipelines.md
    How to organize:

    • Prompt → model response → reviewer feedback
    • Labeling pipelines in tools like Label Studio, Prodigy, Surge AI, etc.

  • Use when: evaluating open-ended generation, dialog quality, or subjective preferences.

Eval Frameworks/ — Tooling to Run Evals at Scale #

  • Goal: Explore libraries and frameworks that let you run, automate, and visualize evaluation workflows.

  • Examples:

    1. helm (Stanford’s Holistic Eval of Language Models)
    2. trl.eval (from HuggingFace’s TRL package)
    3. chat-arena (for pairwise comparison tournaments)
    4. language-evals (emergent libraries focused on LLM evals)

  • Use when: you want to run evals as code, integrate with CI/CD, or do head-to-head model comparisons.

Monitoring_vs_Eval — Operational vs Research Evaluation #

  • Goal: Clarify the difference between offline evaluation and live monitoring in production.

  • Evaluation ≠ Monitoring:

    • Evaluation = Pre-deployment, scenario-specific
    • Monitoring = Post-deployment, continuous observability

  • How feedback loops connect them

  • Why alignment evals don’t end at launch