| # TCB: Trust-Calibration Benchmark for Language-Model Workers |
|
|
| 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) |
| ```bash |
| 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 |
| ```bash |
| 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. |
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