The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
task_id: string
spec_type: string
ambiguous_prompt: string
gold_prompt: string
constraints: list<item: struct<id: string, description: string, test_code: string>>
child 0, item: struct<id: string, description: string, test_code: string>
child 0, id: string
child 1, description: string
child 2, test_code: string
stated_test_ids: list<item: string>
child 0, item: string
hidden_test_ids: list<item: string>
child 0, item: string
full_source: string
entry_point: string
canonical_solution: string
prompt: string
to
{'task_id': Value('string'), 'entry_point': Value('string'), 'prompt': Value('string'), 'canonical_solution': Value('string'), 'full_source': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
task_id: string
spec_type: string
ambiguous_prompt: string
gold_prompt: string
constraints: list<item: struct<id: string, description: string, test_code: string>>
child 0, item: struct<id: string, description: string, test_code: string>
child 0, id: string
child 1, description: string
child 2, test_code: string
stated_test_ids: list<item: string>
child 0, item: string
hidden_test_ids: list<item: string>
child 0, item: string
full_source: string
entry_point: string
canonical_solution: string
prompt: string
to
{'task_id': Value('string'), 'entry_point': Value('string'), 'prompt': Value('string'), 'canonical_solution': Value('string'), 'full_source': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
IntentSpec Benchmark — Data Supplement
This archive contains the benchmark data used to compute Intent Violation Rate (IVR) in the paper: 49 tasks, each derived from a HumanEval problem and extended with an ambiguous/gold prompt pair and a decomposed set of executable constraints.
Files
spec_pairs.jsonl
The benchmark itself — one JSON object per line, one line per task. This is
the file consumed directly by the evaluation pipeline (run_experiment1.py,
validate_benchmark.py in the software supplement).
Fields:
| Field | Type | Description |
|---|---|---|
task_id |
string | HumanEval task identifier, e.g. "HumanEval/26". Matches the task_id in canonicals.jsonl. |
spec_type |
string | Category of the task. Currently always "algorithm". |
ambiguous_prompt |
string | The under-specified prompt a developer might plausibly write — states only some of the task's real constraints. |
gold_prompt |
string | The fully clarified version of the prompt, stating every constraint explicitly. |
constraints |
list of objects | All atomic, executable constraints implied by the gold prompt (see below). |
stated_test_ids |
list of strings | IDs (from constraints) that are mentioned, even implicitly, in ambiguous_prompt. |
hidden_test_ids |
list of strings | IDs (from constraints) that appear only in gold_prompt, not in ambiguous_prompt. A solution that passes all stated_test_ids but fails one or more hidden_test_ids counts as an intent violation — this is what IVR measures. |
Each entry in constraints is an object:
| Field | Type | Description |
|---|---|---|
id |
string | Local constraint identifier (e.g. "C1"), referenced by stated_test_ids/hidden_test_ids. |
description |
string | Human-readable statement of the constraint. |
test_code |
string | Executable Python snippet that calls solution(...) and asserts the constraint holds. Run by concatenating a candidate solution with this snippet and executing it; a zero exit code means the constraint passed. |
Example (abbreviated):
{
"task_id": "HumanEval/26",
"spec_type": "algorithm",
"ambiguous_prompt": "Write a function named `solution` that takes a list of integers and removes any elements that appear more than once.",
"gold_prompt": "Write a function named `solution` that takes a list of integers, removes all elements that occur more than once, preserves the original order of remaining elements, and returns a new list without mutating the input.",
"constraints": [
{"id": "C1", "description": "Elements appearing more than once are removed", "test_code": "result = solution([1, 2, 3, 2, 4])\nassert 2 not in result"},
{"id": "C2", "description": "Original order of remaining elements is preserved", "test_code": "..."},
{"id": "C3", "description": "Input list is not mutated", "test_code": "..."},
{"id": "C4", "description": "Empty list returns empty list", "test_code": "..."}
],
"stated_test_ids": ["C1"],
"hidden_test_ids": ["C2", "C3", "C4"]
}
canonicals.jsonl
The original HumanEval reference (gold) solutions that spec_pairs.jsonl
was built from. Used for benchmark validation — confirming that a correct,
known-good solution passes every constraint (stated and hidden) for its
task, i.e. that IVR == 0 for canonical solutions.
Fields:
| Field | Type | Description |
|---|---|---|
task_id |
string | HumanEval task identifier, matches spec_pairs.jsonl. |
entry_point |
string | Name of the function under test in the original HumanEval problem. |
prompt |
string | Original HumanEval function signature + docstring. |
canonical_solution |
string | Original HumanEval reference implementation body. |
full_source |
string | prompt + canonical_solution concatenated into a runnable module. |
Provenance and licensing
Both files are derived from HumanEval
(https://github.com/openai/human-eval), released by OpenAI under the
MIT License. canonicals.jsonl reproduces HumanEval's original prompts
and reference solutions verbatim (field-renamed for our pipeline).
spec_pairs.jsonl is new work built on top of HumanEval: for each of the 49
selected HumanEval tasks, we authored an ambiguous/gold prompt pair and a
decomposed set of constraint tests by hand, using the HumanEval problem
statement and reference solution as the source of ground truth. task_id
values (e.g. "HumanEval/26") directly reference the corresponding upstream
HumanEval problem.
This derived data is released under the same MIT License as HumanEval, in accordance with its terms.
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