Add dataset card

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - question-answering
+  - text-generation
+language:
+  - en
+pretty_name: Competence-Based Evaluation (Invariance Benchmark)
+size_categories:
+  - 10K<n<100K
+tags:
+  - reasoning
+  - logical-reasoning
+  - invariance
+  - robustness
+  - benchmark
+  - sft
+configs:
+  - config_name: eval_pos
+    data_files:
+      - split: original
+        path: eval/pos/original.jsonl
+      - split: equivalent
+        path: eval/pos/equivalent.jsonl
+  - config_name: eval_pos_largeN
+    data_files:
+      - split: original
+        path: eval/pos_largeN/original.jsonl
+      - split: equivalent
+        path: eval/pos_largeN/equivalent.jsonl
+  - config_name: eval_depth
+    data_files:
+      - split: original
+        path: eval/depth/original.jsonl
+      - split: equivalent
+        path: eval/depth/equivalent.jsonl
+  - config_name: sft_full
+    data_files:
+      - split: train
+        path: sft/full/train.jsonl
+      - split: validation
+        path: sft/full/val.jsonl
+  - config_name: sft_noleak
+    data_files:
+      - split: train
+        path: sft/noleak/train.jsonl
+      - split: validation
+        path: sft/noleak/val.jsonl
+---
+
+# Competence-Based Evaluation (Invariance Benchmark)
+
+A benchmark for testing whether language models give the **same answer** to
+semantically equivalent reformulations of a logical-ordering question. Given a
+set of pairwise constraints (e.g. *Alice is in front of Bob*), a model should
+answer transitive-closure queries (*Is Carol in front of Dave?*) consistently
+whether the constraints are stated using a relation or its inverse.
+
+Each item exists as a paired (`original`, `equivalent`) record describing the
+same underlying ordering with different surface phrasings. **Invariance** is
+measured as the agreement between the model's `original` and `equivalent`
+answers; **accuracy** is measured against the ground-truth boolean.
+
+## Subsets
+
+### Evaluation (held-out)
+
+| Config | Split | Rows | N range | Notes |
+|---|---|---|---|---|
+| `eval_pos` | `original`, `equivalent` | 4,000 each | 4–2048 | Main yes/no eval. Uses the held-out `pos` (in-front-of/behind) relation. Names list shown in the prompt is shuffled to remove the order-of-names leak. |
+| `eval_pos_largeN` | `original`, `equivalent` | 1,200 each | up to several thousand | Stress test at large N. |
+| `eval_depth` | `original`, `equivalent` | 2,000 each | 4–64 | Held-out `depth` (above/below stacking) relation, names-list shuffled. |
+
+Each row is one yes/no question. Within a config, row `i` of the
+`original` split and row `i` of the `equivalent` split describe the **same
+underlying ordering** and the **same query**, only with the relation phrased
+differently (e.g. "Alice in front of Bob" vs. "Bob behind Alice"). They share
+the same ground-truth answer.
+
+Schema:
+
+```json
+{
+  "question": "There are 4 people standing in some order.\nTheir names are [...]\n...\nIs Nicholas in front of Thomas? Provide your answer only as yes or no. Answer: \n",
+  "answer": "yes",
+  "is_fwd": true,
+  "num_elements": 4
+}
+```
+
+### Supervised fine-tuning
+
+The SFT subsets are chat-formatted (`messages` field) and ready for
+`trl.SFTTrainer` / OpenAI fine-tuning. They are built from a different set of
+fact-agnostic relations than the eval set, with `n` skewed toward small values.
+Each underlying ordering is expanded across `(is_fwd, answer)` combinations
+× `(original, equivalent)` phrasing = 8 rows.
+
+| Config | Split | Rows | Train relations | Notes |
+|---|---|---|---|---|
+| `sft_full` | `train` | 45,600 | arrival, priority, proximity, seniority, spatial_lr, spatial_ud | All fact-agnostic relations. |
+| `sft_full` | `validation` | 2,400 | (same) | In-distribution validation split. |
+| `sft_noleak` | `train` | 45,600 | (same as `sft_full`) | Built with `--shuffle-names-display` to remove the names-list leak; **this is the version used for the paper's reported fine-tuning results**. |
+| `sft_noleak` | `validation` | 2,400 | (same) | In-distribution validation split. |
+
+The `pos` and `depth` relations are **deliberately excluded** from training so
+that the eval subsets remain genuinely out-of-distribution.
+
+Schema (chat / messages format):
+
+```json
+{
+  "messages": [
+    {"role": "system", "content": "You are a helpful assistant. Answer logical reasoning questions concisely."},
+    {"role": "user", "content": "There are 8 employees ... Is Juana more senior than Felecia? ..."},
+    {"role": "assistant", "content": "no"}
+  ]
+}
+```
+
+Per-config metadata (n distribution, per-relation counts, seed) lives in
+`sft/full/meta.json` and `sft/noleak/meta.json`.
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+# Eval — paired splits, same row index = same underlying ordering.
+ds = load_dataset("jizej/Competence-Based-Evaluation", "eval_pos")
+org = ds["original"]
+eqv = ds["equivalent"]
+
+# SFT — chat-formatted.
+sft = load_dataset("jizej/Competence-Based-Evaluation", "sft_noleak")
+train = sft["train"]
+```
+
+## Construction
+
+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`.
+
+## License
+
+Released under **CC BY 4.0**. Entity names sourced from Wikidata/Wikipedia
+retain their original licenses (CC0 / CC BY-SA).
+
+## Citation
+
+Please cite the accompanying paper if you use this dataset (citation TBD —
+NeurIPS 2026 Datasets & Benchmarks track submission).