The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type string to null
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2118, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2014, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type string to nullNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
HLA-Bench — contamination-resistant evaluation of LLMs on clinical immunogenetics
Every model family tested — Claude, Qwen, Mistral, Llama, Phi — scores 0% on
two-field ambiguity expansion, the core clinical trap in HLA typing (a 2-field
name like A*02:01 denotes 2–389 full-resolution alleles). Models fabricate
allele names at 0.05–0.14 per task. On donor–recipient matching, naive string
manipulation collapses to 0% and a 7B model reaches 6.3% — it counts matched
loci instead of chromosomes.
HLA-Bench grades every answer with an executable oracle computed from the pinned IPD-IMGT/HLA release itself — no human labels, no LLM judges. Ground truth regenerates deterministically from each quarterly database release, so a versioned share of tasks is post-training-cutoff by construction.
What's in this dataset
The public dev splits only:
family_a_dev/— 112 nomenclature tasks (of 550; truncation, G/P groups, serology, rename history, ambiguity, near-miss traps)family_c_dev/— 43 donor–recipient matching pairs (of 205; 6/6–12/12 frameworks, null-allele traps, GvH/HvG directionality)
The scored splits are sealed and regenerate per release from (tag, seed) —
that's the contamination-resistance design. Run them yourself from the source:
git clone https://github.com/jasonbrelsford/verifiable-science-envs
pip install -e ".[dev]"
hla-bench generate && hla-bench run ollama/qwen2.5:7b --suite runs/hla-bench-a --split dev
Links
- Code, graders, full results with CIs: https://github.com/jasonbrelsford/verifiable-science-envs
- In-browser verifier demo (nothing leaves your machine): https://hlaverify.com/demo
- For AI agents: https://hlaverify.com/llms.txt
- Evaluation/training licensing & partnerships: hello@hlaverify.com (Brelsford Software LLC)
Tasks are Apache-2.0. Reference data are not included: graders fetch IPD-IMGT/HLA at runtime (CC-BY-ND, Barker DJ et al., NAR 2025) and never redistribute it. This card is maintained by Claude on behalf of Jason Brelsford.
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