Datasets:

ai4he
/

trust-calibration-benchmark

A small, auditable qualification test for language models used as components of multi-LLM pipelines: 60 yes/no items across five domains (arithmetic, dates, geography, science, language), each tagged easy or hard, with audited gold answers. A model answers ANSWER: Yes|No and CONF: 0-100; we score accuracy, expected calibration error (ECE), Brier, yes-rate, strict-format adherence, and availability, under an AUDITED administration protocol (template-guarded parsing, answers only from tagged or bare final lines, escalate-on-unparseable budgets, imputation flags).

Files

../data/tcb_benchmark.json — the 60 items (id, question, gold truth, tier), the prompt, and scoring definitions. v0.2.1 corrects one gold label (g7: Astana IS the capital of Kazakhstan), found by the independent audit of all 60 labels (../data/label_audit.json).
../data/tcb_v03_trials.json — per-trial records, validated (adequate) protocol.
../data/tcb_v03_trials_constrained.json — audited parser, fixed 512-token budget, no escalation (unparseable = unavailable).
../data/tcb_v03_trials_naive.json — the naive-protocol records. The naive and constrained protocols consumed the SAME generations; derive_naive.py re-derives every naive verdict offline from the constrained file's full raw responses using the historical parser (preserved verbatim inside it), with 0/540 mismatches against the original run.
../data/tcb_repeat_results_naive.json — the naive-protocol resampling summary (the fabricated flip rates), alongside the validated-protocol repeat files.
../data/tcb_harness_comparison.json — the three-protocol comparison summary.
../data/tcb_repeat_{results,trials}.json — run-to-run variation (3 repeats, temp 0.7).
../data/tcb_decouple{,_trials}.json — the confidence-elicitation manipulation.
../data/tcb_criterion{,_trials}.json — the transfer study (SST-2 annotation + battery).
analyze_panel.py — canonical analyzer: Table 1, figures, pairwise exact-McNemar + BH, from the shipped validated-protocol trials. Stdlib + matplotlib only.
analyze_harness.py — the three-protocol harness-validity comparison (figure + JSON).
analyze_decouple.py — Q3 re-analyzer: per-model per-variant accuracy/ECE/mean-confidence from tcb_decouple_trials.json, checked against tcb_decouple.json (stdlib only, no endpoint).
analyze_criterion.py — Q4 re-analyzer: per-task accuracy/ECE, benchmark-to-downstream Spearman with exact permutation p-values, gate split, and gate-pass/fail calibration ratios with and without the leanstral-2603 outlier, from the shipped trials (stdlib only, no endpoint, no HuggingFace download); Spearman values checked against tcb_criterion.json.
analyze_classwise.py — camera-ready additions: per-model confusion matrices, per-class precision/recall/F1, macro-F1, balanced accuracy (writes ../tables/tcb_classwise.tex, with pooled validated and naive rows), plus the confidence-resolution statistics (accuracy at stated confidence below 100 vs at 100, pooled AUROC of stated confidence for correctness) and the panel accuracy-vs-ECE Spearman correlation. Stdlib only.
sensitivity.py — ECE with vs without confidence-imputed trials.
tcb_parsing.py — the audited parser + escalation rule (single canonical implementation).
run_tcb.py — evaluate a new model (one command; appends to the panel).
run_panel_v03.py (in the project tree), run_repeat_v03.py, run_decouple.py, run_criterion.py — generation scripts (need an endpoint; all outputs ship).
analyze_tcb.py — historical v0.2 analysis, kept for provenance; writes its table fragment to tables/tcb_results_v02.tex so it never overwrites the canonical 9-model tcb_results.tex.

Reproduce the paper (no model queries needed)

python3 analyze_panel.py      # Table 1, reliability + forest figures, pairwise tests (Q1)
python3 analyze_harness.py    # the three-protocol comparison (Figure 3 + summary JSON, Q2)
python3 analyze_classwise.py  # confusion matrices + per-class P/R/F1 table, confidence resolution
python3 analyze_decouple.py   # the confidence-elicitation manipulation (Q3)
python3 analyze_criterion.py  # the transfer study: Spearman + permutation p + gate split (Q4)
python3 sensitivity.py        # imputation sensitivity
python3 derive_naive.py       # re-derive the naive condition from shipped raws (prints 0/540)

Every table, figure, and statistical test in the paper is reproduced offline by the commands above from the shipped per-trial data; only the run_*.py generators need a model endpoint.

Evaluate a new model / audit a new harness

python3 run_tcb.py <model-name> > my_trials.json

Append the rows to tcb_v03_trials.json and re-run analyze_panel.py; extra models are detected automatically. To audit a different harness, run its parser over the shipped raw responses and compare verdicts against tcb_v03_results.json.

Metrics

Accuracy (Wilson 95% CI), over parseable responses; availability reported separately.
ECE, 5 equal-width bins, bootstrap CIs; imputed-confidence trials count (a missing confidence is an unusable signal), with stated-only sensitivity via sensitivity.py.
Brier with bootstrap CIs; pairwise exact binomial McNemar, Benjamini-Hochberg corrected.
Yes-rate vs the 0.60 gold base rate; strict-format adherence out of 60.