Datasets:
Guide to evaluating type-safe models
Here, type-safe models means models evaluated through fixed-choice interfaces. “Type-safe” describes the output format, not decision correctness or safety. This grouping does not imply a shared architecture or affiliation with TypeSafe.
This release turns XSTest, OR-Bench, and HarmBench requests into fixed-choice questions. It adds original, reversed, and order-only versions so you can check both correctness and whether answers change. Credit the original benchmark authors for the requests and labels; their papers and dataset documentation are linked in the dataset card.
Three input views
Relative to the deterministic original assignment, reversal changes both key-to-description assignment and description presentation order. The serialization-only control preserves each key-description association while reversing its presentation order:
| Variant | Key-description binding | Description order | Evaluation status |
|---|---|---|---|
| original | Original | Original | All five models |
| reversed | Swapped | Reversed | All five models |
| serialization_control | Original | Reversed | Original Jev only |
For example, original [A: benign, B: harmful] becomes reversed [A: harmful, B: benign] and serialization control [B: harmful, A: benign]. The state, rubric, description strings and semantic target are identical. Do not send semantic_label or gold_label to the model. Ordered arrays, not a sorted JSON map, are authoritative.
These transformations describe the client input. A provider may canonicalize object keys; a plain-option adapter may discard keys entirely. Therefore, do not infer internal causal mechanisms from client-side manipulations alone. For key-agnostic adapters, reversed/serialization-control are identical native inputs. Keep actual rendering and request hashes. Only Original Jev has order-only results; all five models have original/reversed results.
What to report
Run the same cases in paired conditions. Report benchmark-specific benign correctness/false-alarm rates and harmful correctness/miss rates, plus counts and coverage. Copyright uses its own labels. For each paired perturbation, report semantic flip rate, both-conditions-correct rate, and stable-wrong rate. A model that always gives the same incorrect answer is stable but inaccurate. An always-target classifier reaches 100% on single-target panels; include that baseline.
Use tools/score_predictions.py for model/panel reports. It accepts semantic predictions or native A/B choices, normalizes by the exact variant mapping, rejects duplicates or inconsistent label/key pairs, separates repeats, and can compare probability changes by semantic label. Different models must be compared on the same supported examples. The common-primary-cohort section compares original-condition results on the intersection of supplied models, separately per panel; it does not claim that later conditions share that cohort. Missing predictions and explicit unsupported records remain distinct.
case_index defines content_group_id as the SHA256 of the exact full state. Rows sharing that ID are exact prompt duplicates across or within source collections. Keep all provided rows for source-faithful scores, but do not pool overlapping panels as independent evidence. If creating a train/test split, keep every duplicate group and every option variant on the same side. This release does not declare a new held-out train/test partition. Conflicting source targets or rubrics are flagged for inspection, not silently repaired.
Interpreting the comparisons
- Report accuracy and changed answers separately. Laya gets 273/655 OR-Bench Toxic requests correct in both orders, but 56 answers change: 28 become correct and 28 become incorrect. The unchanged score hides those changes.
- Compare benign and harmful results together. Original Jev outperforms Laya on OR-Bench Toxic, while Laya outperforms Original Jev on OR-Bench Hard. These measure different error types; neither column alone establishes a generally better model.
- Keep tasks and source splits separate. HarmBench copyright uses a different rule from harmful/benign classification. Small validation sets can produce large percentage changes from a few answers.
- Account for model interfaces. CLM scores candidate texts separately. Unchanged answers under reordering are consistent with that design; they do not, by themselves, demonstrate better learned robustness. Compare the actual inputs in CONFIGURATIONS.md.
- Do not isolate causes this test cannot separate. Reversal changes both order and A/B assignments. Original Jev's order-only control can also reflect provider processing and run variation. Subtracting the two flip rates does not establish an A/B-assignment effect.
The adapter snapshots document the formats; they are not a complete runnable environment. See CONFIGURATIONS.md for dependencies and model-version limits. Pin the dataset revision and record the actual model version returned by the provider.
Prediction format
One JSONL record per (model, pair_id, variant, repeat_index):
{"model":"my-model","pair_id":"XSTest:xstest.csv:1","variant":"original","chosen_key":"A","repeat_index":0}
chosen_key is interpreted using that exact row. Alternatively supply predicted_label. Optional probabilities may be keyed by native A/B keys or by semantic labels. If both key and label are present they must agree. status:"unsupported" records an explicit exclusion; it is not scored as incorrect. Omitting a record means missing, not unsupported. Repeats use a positive repeat_index and are compared to repeat zero without inflating primary denominators.
python tools/score_predictions.py predictions.jsonl --output report.json
python -m unittest discover -s tools -p 'test_score_predictions.py'
All three request views remain available through the requests configuration. Results and exact model configuration metadata are separate. All five model evaluations are complete for the supported original/reversed inputs. Only Original Jev was evaluated on order-only inputs. Skipped and missing cases are reported separately. Aggregate results are presented in README.md. Normalized individual answers are published under model_results. Run python tools/score_release.py to recompute and verify the aggregate report. See RESULTS_SCHEMA.md for joins and field meanings. The prediction scorer reports multiclass Brier scores descriptively on supplied probabilities, without claiming population calibration.
Contributions and label review
Share new model predictions in the above format with checkpoint/version, native rendering, implementation hash, inference settings, and unsupported reasons. Keep the original inputs/rubric fixed for comparable baseline results. Put changed wording, extra key names or calibration policies in a separately named experimental condition. Propose disputed source labels with source ID, original target, rationale and supporting evidence; this release does not infer a replacement gold label from model disagreement.
Related work
Type-Safe Is Not Error-Free already studies option-name/rubric assignment in Jev and open alternatives. Typed Decision Models: An Early Evidence Audit and Evaluation Checklist emphasizes accuracy/latency tradeoffs and evaluation quality. Beyond Calibration examines probability coherence. This kit contributes attributed full-text coverage, reusable controlled inputs, paired error analyses and traceable inference settings; it does not claim first discovery of option sensitivity or proven calibration.
Jev: Decision Models on Trial (Arun and Ma, 2026) also evaluates Jev and Laya for safety classification, using a different taxonomy and model-generated reference labels. Our release uses original benchmark labels and matched choice variants. Neither choice sensitivity nor Jev safety evaluation is a first-discovery claim here.