Add Limitations, Biases, PII, Use Cases, Social Impact, Source Datasets, Provenance sections (Croissant RAI fields)

README.md

@@ -136,12 +136,303 @@ sft = load_dataset("jizej/Competence-Based-Evaluation", "sft_noleak")
 train = sft["train"]
 ```
 
-## Construction
+## Source Datasets
 
-Datasets are generated procedurally from entity pools sourced from Wikidata,
-Wikipedia, and curated lists. The generator and entity-pool fetcher are open
-source at the project repository (linked above). All generation seeds are
-recorded in the per-subset `meta.json`.
+The dataset is fully synthetic — no records are copied from another corpus.
+However, the procedural generator draws **entity names** (people, animals,
+cities, structures, etc.) from external knowledge sources. The complete list
+of upstream source URIs is:
+
+- **Wikidata SPARQL endpoint:** `https://query.wikidata.org/sparql`
+  Used for the `size_animals`, `height_structures`, `age_figures`,
+  `time_events`, `brightness_stars`, and `speed_animals` (Wikidata fallback)
+  pools. Queries are stored verbatim in
+  [`invariance_bench/generate_entities.py`](https://github.com/jizej/Competence-Based-Evaluation/blob/main/invariance_bench/generate_entities.py).
+- **English Wikipedia REST API:** `https://en.wikipedia.org/api/rest_v1/page/html/...`
+  Specific source pages:
+    - `https://en.wikipedia.org/wiki/List_of_cities_by_average_temperature`
+      (`temperature_cities` pool)
+    - `https://en.wikipedia.org/wiki/Fastest_animals` (fallback for
+      `speed_animals`)
+- **Curated lists** embedded in the generator script (no external URI):
+  `weight_objects`, `price_items`, `rank_athletes`, `spatial_objects`, and
+  the names list used by the `pos` and SFT relations. These are author-
+  maintained and are the only non-Wikidata/Wikipedia sources.
+
+Wikidata content is licensed CC0 and Wikipedia text is licensed CC BY-SA.
+Cached responses for every pool are stored under `.entity_cache/{pool}.json`
+in the open-source repository so the dataset can be regenerated bit-exact
+without re-querying the upstream sources.
+
+**Synthetic-generation seeds** that fully determine the released splits are
+recorded in the per-subset `meta.json` files (`sft/full/meta.json`,
+`sft/noleak/meta.json`); the seed used for the released SFT subsets is
+`42`. Eval splits use deterministic enumeration over `(N, ordering, query)`
+triples and require no random seed.
+
+## Provenance Activities
+
+The end-to-end activities applied to produce this dataset are:
+
+1. **Collection (automated, online).** Entity pools fetched from Wikidata
+   via SPARQL and from Wikipedia via the REST API; see
+   [`invariance_bench/generate_entities.py`](https://github.com/jizej/Competence-Based-Evaluation/blob/main/invariance_bench/generate_entities.py).
+   Rate-limited with retries; results cached on disk.
+2. **Cleaning / filtering.** Per-pool deduplication (case-insensitive name
+   collapsing), removal of entries missing the relevant ground-truth value,
+   and merging of SPARQL results with curated fallback lists. For
+   `age_figures` the SPARQL query is split into three era-based
+   sub-queries to avoid `wikibase:sitelinks`-induced timeouts.
+3. **Curated fallback authoring.** Manual curation by the dataset authors
+   for the `weight_objects`, `price_items`, `rank_athletes`,
+   `spatial_objects`, and `names` pools (lists embedded directly in
+   `generate_entities.py` and `question_generation.py`).
+4. **Synthetic question generation.** Procedural construction of the
+   eval and SFT records in
+   [`invariance_bench/question_generation.py`](https://github.com/jizej/Competence-Based-Evaluation/blob/main/invariance_bench/question_generation.py)
+   and the entry-point scripts
+   [`scripts/generate_dataset.py`](https://github.com/jizej/Competence-Based-Evaluation/blob/main/scripts/generate_dataset.py),
+   [`scripts/generate_heldout_dataset.py`](https://github.com/jizej/Competence-Based-Evaluation/blob/main/scripts/generate_heldout_dataset.py),
+   and
+   [`scripts/generate_training_data.py`](https://github.com/jizej/Competence-Based-Evaluation/blob/main/scripts/generate_training_data.py).
+   This step is fully deterministic given the seed and entity pools.
+5. **Annotation.** None. There is no human-annotation step. All
+   `answer` / `messages` ground-truth labels are produced by the same
+   deterministic generator that creates the question text, and are derived
+   from the synthesized ground-truth ordering, not from human judgment.
+6. **Synthetic agents / LLMs.** **None.** No language model, embedding
+   model, or generative agent is used at any step in the pipeline.
+7. **Crowdsourcing platforms / human teams.** Not applicable — no
+   crowdsourcing, no human raters, no annotation contractors were
+   involved.
+8. **Validation / leak audit.** The released `_shufnames` / `noleak`
+   subsets were produced after an internal audit revealed that an earlier
+   version's prompt-side names list ordering correlated with the answer.
+   The audit is documented in `docs/paper_methodology_experiments.md` of
+   the open-source repository.
+
+## Construction (Synthetic-Data Generation Process)
+
+**All records in this dataset are synthetic.** They are produced by a
+deterministic procedural generator; no model-based generation, no human
+annotation, and no scraped natural-language Q&A is used in the pipeline.
+
+The generation process is:
+
+1. **Entity pools.** Names of entities (animals, structures, people, cities,
+   events, stars, etc.) are sourced from Wikidata SPARQL queries, Wikipedia
+   HTML tables, and small curated fallback lists embedded in the generator.
+   Each pool is cached on disk as JSON. See
+   [`invariance_bench/generate_entities.py`](https://github.com/jizej/Competence-Based-Evaluation/blob/main/invariance_bench/generate_entities.py).
+2. **Ordering sampling.** For each (relation, `n`) bucket the generator
+   samples a random permutation of `n` entities from the appropriate pool
+   and lays out the chain implied by the relation (e.g. *front-of* / *behind*).
+3. **Constraint expansion.** A subset of consecutive pairs is selected to
+   form the "rules" shown in the prompt; the unstated remainder is what the
+   transitive-closure query exercises.
+4. **Phrasing duplication.** Every ordering is rendered twice: once with
+   the canonical relation (`original`) and once with the logically inverse
+   relation (`equivalent`). The two renderings carry the same ground-truth
+   boolean answer.
+5. **Yes/no query selection.** A query pair `(a, b)` is sampled at a
+   configured minimum hop distance, with the ground-truth `yes`/`no` answer
+   balanced by construction.
+6. **(SFT subsets only) Chat formatting.** Each (ordering, query, phrasing)
+   triple is serialized into a `messages` array with system / user /
+   assistant turns ready for SFT trainers.
+
+All generation seeds, the per-relation count distributions, the `N`
+schedule, and the held-out relation list are recorded in the per-subset
+`meta.json`. The full pipeline is reproducible from the open-source
+repository linked above.
+
+## Intended Use Cases
+
+The dataset is designed to measure **answer-level invariance** of language
+models under semantically-preserving paraphrasing of logical-ordering
+constraints. Concretely:
+
+- **Primary use case (validated):** measuring whether a model returns the
+  same boolean answer to a transitive-closure query when the underlying
+  ordering is described with a relation versus its inverse. Validation is
+  reported in our accompanying NeurIPS 2026 D&B submission across
+  proprietary and open-weight models.
+- **Primary use case (validated):** comparing pre- and post-fine-tuning
+  checkpoints to verify that targeted SFT improves invariance without
+  destroying out-of-distribution generalization (held-out `pos` and `depth`
+  relations).
+- **Secondary use case (partially validated):** scaling-law style analyses
+  of invariance vs. accuracy as a function of `N` (the number of entities
+  in the ordering). Validated for `N ∈ [4, 2048]`; behavior beyond this
+  range is not characterized.
+- **Secondary use case (not validated here):** as a regression test for
+  training pipelines that aim to preserve symbolic reasoning under
+  paraphrase. We provide the data; we do not certify any specific training
+  recipe.
+
+Use cases for which validation **does not** exist or may not hold:
+general-reasoning leaderboard ranking, safety / alignment evaluation,
+detection of jailbreaks or adversarial prompts, multilingual robustness,
+evaluation of long-form generation quality, and any clinical, legal, or
+high-stakes decision-support setting.
+
+## Personal and Sensitive Information
+
+The dataset contains **no real personal data, no real PII, and no health,
+medical, financial, biometric, political, or religious data about
+identifiable individuals**. All "people" in the prompts are synthetic
+references constructed by sampling from entity pools.
+
+The following indirect demographic signals are present and should be
+declared:
+
+- **Gender (indirect, via names).** First names sampled from US-style name
+  lists carry conventional masculine/feminine associations. No gender label
+  is attached to any record; gender is only implicit in the name token.
+- **Geography.** Pools such as `temperature_cities`, `height_structures`,
+  and `time_events` contain real geographic place names sourced from
+  Wikidata and Wikipedia. These pools are skewed toward globally prominent,
+  English-Wikipedia-covered locations.
+- **Language.** Prompts and answers are exclusively in English; this is a
+  deliberate scope restriction, not a privacy signal, but it is recorded
+  here for completeness.
+- **Culture.** Entity selection inherits the cultural skew of Wikidata /
+  English Wikipedia (Western, anglophone over-representation).
+- **Age (of historical figures only).** The `age_figures` pool references
+  real historical figures with their public birth years. These are
+  deceased public figures whose biographical data is already published on
+  Wikidata; no contemporary individuals' ages are present.
+
+The following are **not** present: socio-economic status of identifiable
+individuals, professional experience or seniority of identifiable
+individuals (the `seniority` and `priority` relations operate on synthetic
+placeholders, not on real employees or rankings), health or medical data,
+political affiliation, and religious belief.
+
+No data subjects were contacted or surveyed in producing this dataset, so
+no consent or withdrawal procedures apply. Wikidata is licensed CC0 and
+Wikipedia is licensed CC BY-SA; both permit redistribution of the entity
+metadata used here.
+
+## Social Impact
+
+**Intended positive impact.** Releasing a clean invariance benchmark
+encourages the field to evaluate language models on robustness to
+paraphrase, not only on accuracy. Reproducible held-out splits and an
+open-source generator make it harder for the benchmark to be quietly
+over-fit, and the SFT subsets give researchers a concrete starting point
+for studying targeted invariance training.
+
+**Potential negative impact and risks of misuse.**
+
+- *Over-claiming general reasoning.* High invariance scores on this
+  dataset measure invariance on transitive ordering only. A naive reader
+  could mistake them for evidence of general reasoning robustness; results
+  should always be reported with the scope of the benchmark stated.
+- *Skill leaderboarding pressure.* As with any public benchmark, optimizing
+  directly against this dataset risks Goodharting — gains here may not
+  transfer to natural-language reasoning. We encourage reporting paired
+  held-out evaluations from other benchmarks.
+- *Cultural / linguistic skew.* Because entity pools are anglocentric,
+  models tuned on this data may improve on similarly-distributed inputs
+  while showing little transfer to non-English or non-Western surface
+  forms.
+- *Indirect demographic correlations.* US-style first names carry
+  conventional gender signals. If a downstream model is trained on the SFT
+  subsets in a way that picks up name-conditioned heuristics, that bias
+  will propagate. Users training on this data should audit for gendered
+  response patterns.
+
+**Mitigations in this release.**
+
+- The dataset is open-license (CC BY 4.0) but **gated by deliberate
+  narrowness of scope** rather than access controls: every record is
+  explicitly a synthetic transitive-ordering question, and the dataset
+  card states the intended-use boundaries above.
+- Held-out relations (`pos`, `depth`) are **excluded from the SFT
+  subsets** so OOD generalization claims remain defensible.
+- The earlier internal `_shufnames` / `noleak` audit (where the displayed
+  names list accidentally encoded the answer) is documented above; the
+  released eval and SFT files have the leak fixed.
+- The generator is open-source, allowing external auditors to reproduce
+  every record from a documented seed.
+
+No usage gating, embargo, or differential-access controls are applied.
+Users are expected to follow the limitations and intended-use guidance
+above and to cite the dataset when reporting results.
+
+## Limitations
+
+- **Narrow reasoning skill.** Each question tests transitive closure over a
+  linear ordering induced by a single binary relation. Performance here does
+  not generalize to multi-step natural-language reasoning, common-sense
+  inference, math, code, or any non-ordering relational structure.
+- **Synthetic phrasings.** Questions are produced by a small grammar (a fixed
+  template per relation) rather than written by humans, so surface-form
+  diversity is limited. Distributional gaps relative to natural prose,
+  conversational queries, or noisy real-world text are large.
+- **English only.** All prompts and answers are English. The benchmark says
+  nothing about cross-lingual robustness.
+- **Yes/no output space.** The eval rewards a literal `yes` or `no` token.
+  Models that hedge, refuse, or emit verbose chains of thought without a
+  committed answer score zero on accuracy and invariance regardless of
+  whether the underlying reasoning is correct. Practitioners using CoT-style
+  models should add an answer-extraction step (see `invariance_bench/scoring.py`).
+- **Single deterministic ground truth.** The eval does not measure
+  calibration, uncertainty, or partial credit; orderings with ties or
+  under-specified constraints are not represented.
+- **Long-context confound.** At large `N` (especially in `eval_pos_largeN`
+  and the `N=2048` slice of `eval_pos`), prompts can exceed the effective
+  context window of many models. Failures at large `N` may reflect context
+  handling rather than reasoning ability and should not be interpreted as
+  pure invariance violations.
+- **Held-out coverage.** The OOD evaluation surface is two relations (`pos`,
+  `depth`); the benchmark cannot verify whether a model's invariance
+  generalizes to relations beyond those seen at train *and* eval time.
+- **Names-list leak in earlier internal versions.** Released `_shufnames`
+  eval files and the `sft_noleak` training files **do not** have this leak.
+  Older `base2_*` artifacts (not released on HF) did, and any third-party
+  reuse of those files would over-estimate model performance.
+
+**Not recommended for:** general reasoning leaderboards, safety/alignment
+evaluation, multilingual evaluation, evaluating models whose primary output
+mode is a long chain of thought without an extractable boolean answer.
+
+## Biases
+
+- **Anglo/Western entity skew.** The `names` pool used by the `pos`-relation
+  questions and by the SFT data is drawn from US-style first-name lists, so
+  most prompts contain English-coded given names. The `temperature_cities`,
+  `height_structures`, and `time_events` pools likewise over-represent
+  Wikipedia/Wikidata-prominent (largely Western, English-language)
+  entities. Under-represented populations include non-Western cultures and
+  languages whose entities have lower Wikipedia coverage.
+- **Source-driven content bias.** Wikidata and Wikipedia are themselves known
+  to be skewed toward male, Western, and modern-era subjects (especially in
+  `age_figures`). The benchmark inherits these biases. Curated fallback
+  lists for `weight_objects`, `price_items`, and `rank_athletes` reflect the
+  authors' own selections and are not demographically balanced.
+- **Relation-template bias.** Each relation has one canonical phrasing and
+  one inverse phrasing. The grammar does not exercise the full space of
+  English ways to express ordering (passive voice, comparative clauses,
+  idiomatic expressions, etc.), so reported invariance is a conservative
+  lower bound: a model that is invariant on this dataset may still be
+  sensitive to other surface variations.
+- **Position-of-name leak (mitigated).** In an earlier internal version,
+  the order of names listed in the prompt correlated with their position in
+  the underlying ordering, which models could exploit without reading the
+  rules. Released eval files (`*_shufnames.jsonl`) and the `sft_noleak`
+  subset shuffle the displayed names list to remove this leak. Users
+  regenerating data with the included scripts must pass
+  `--shuffle-names-display` to reproduce the no-leak setting.
+- **Train/eval relation leakage controls.** `pos` (front/behind) and `depth`
+  (above/below) are deliberately held out of the SFT data so they remain
+  OOD for fine-tuned checkpoints. Mixing the SFT subsets with held-out
+  evaluation defeats the OOD claim.
+- **Per-`N` row-count imbalance.** Both eval and SFT skew toward small `N`
+  (the SFT distribution explicitly down-weights large `N`). Aggregate
+  metrics across `N` are therefore dominated by the small-`N` regime;
+  report per-`N` numbers when comparing models.
 
 ## License