How do you test sampling strategies (e.g., temperature, top-k)?

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1) Define the goal → pick metrics

Different tasks need different yardsticks.

Goal	Good metrics
Factual QA / reasoning	Exact-Match / F1 (SQuAD-style), accuracy on GSM8K/BBH, pass@k (code)
Creativity/fluency	Human preference (pairwise win-rate), MAUVE, self-BLEU (lower is better diversity), Distinct-n, repetition rate
Helpfulness/safety	Preference win-rate vs baseline, refusal rate (when appropriate), toxicity/PII leakage rates, jailbreak success rate
Stability/determinism	Variance across seeds, entropy of next-token distribution, output edit distance across runs
Latency/cost	Tokens/sec, total tokens generated, time-to-first-token

2) Experimental design (robust & reproducible)

1. Fix the model & prompts (same system prompt, same stop tokens).

2. Grid/sweep decoding knobs:

   • temperature ∈ {0.0, 0.2, 0.5, 0.7, 1.0}

   • top-k ∈ {0, 40, 100} (0 = disabled)

   • top-p ∈ {0.8, 0.9, 0.95} (don’t mix top-k and top-p unless you intend to constrain both).

3. Multiple seeds & samples: e.g., 5 seeds × 3 samples/prompt to estimate variance.

4. N prompts per task: 100–1,000 for reliable stats.

5. Evaluate with automated metrics + blind human pairwise judgments on a subset.

6. Stat tests: paired t-test or Wilcoxon on per-prompt scores; bootstrap CIs for win-rates.

3) What to look for (typical patterns)

• Temperature ↑ → diversity ↑, determinism ↓, factuality often ↓.

  • Use 0–0.3 for deterministic tasks (math, code), 0.7–1.0 for ideation.

• Top-p (nucleus) ~0.9 often balances quality/diversity; lower p trims tail risk.

• Top-k ~40 is a common sweet spot; higher k can add noise; k=0 disables it.

• Beam search can help short, exact tasks but risks degenerate repetition; avoid for open-ended generation.

• Typical sampling stabilizes phrasing by following “typicality” of tokens; test vs top-p on long-form.

4) Concrete test battery (plug-and-play)

• QA/Reasoning: SQuAD (EM/F1), GSM8K (accuracy), BBH—temperature sweep at top-p=0.9.

• Code: HumanEval / MBPP—measure pass@1, pass@5 under temp ∈ {0.0, 0.2} and top-p ∈ {0.95}.

• Creative writing: Human pairwise preferences + MAUVE, Distinct-2/3; temp ∈ {0.7, 1.0}, top-p ∈ {0.9, 0.95}.

• Safety: Red-teaming prompts set—track toxicity/jailbreak rate vs temp/top-p; typically lower temp reduces risk.

5) Quality & robustness checks

• Seed sensitivity: report mean ± std across seeds.

• Repetition/looping: n-gram repeat rate, longest repeated span.

• Hallucination audits: fact-checking subset with retrieval-augmented oracle.

• Cost: token counts—higher temps often lengthen outputs.

6) Example eval harness (pseudocode)

for cfg in grid(temperature=[0.0,0.2,0.5,0.7,1.0], top_p=[0.9], top_k=[0]):
    scores = []
    for prompt in prompts:
        outs = [generate(prompt, **cfg, seed=s) for s in seeds]
        best = select_for_metric(outs)  # e.g., most factual or first sample
        scores.append(metric(prompt, best))
    summarize(cfg, mean(scores), ci(scores))

7) Recommended defaults (start here)

• Deterministic tasks: temp 0–0.2, top-p 0.9, no top-k.

• Balanced chat: temp 0.5, top-p 0.9.

• Creative: temp 0.8–1.0, top-p 0.95.

• Avoid combining top-k and top-p unless you’ve profiled the trade-off.

8) Report template (what stakeholders want)

• Setup (model/version, prompts, seeds, stop conditions)

• Config table (decoding params)

• Metrics with CIs + significance

• Cost/latency

• Safety outcomes

• Recommendation (config per use-case)

Read more :

What is reproducibility in Gen AI testing?

How do you test for prompt sensitivity?

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