Expand GenomeQA dataset card

Document benchmark purpose, provenance, task families, configuration meanings, schema, usage, balanced subsets, and citation.

README.md

@@ -1,4 +1,16 @@
 ---
+pretty_name: GenomeQA
+language:
+- en
+task_categories:
+- question-answering
+tags:
+- biology
+- bioinformatics
+- genomics
+- dna
+- benchmark
+- multiple-choice
 dataset_info:
 - config_name: binary
   features:
@@ -111,11 +123,94 @@ configs:
     path: mcq_600/train-*
 ---
 
-# Balanced 600-example subsets
+# GenomeQA
 
-The `binary_600` and `mcq_600` configurations each contain 100 examples sampled without replacement from each of
-GenomeQA's six subtasks. The combined rows are shuffled so the configurations are not stored in subtask blocks.
+GenomeQA is a diagnostic question-answering benchmark for evaluating whether general-purpose language models can
+reason directly over raw nucleotide sequences. It presents English questions containing one or more DNA sequences and
+asks the model to select an answer using biological signals such as sequence composition, motifs, and motif context.
+It does not assume a specialized DNA encoder or task-specific model training.
 
-The subsets were generated with seed 42 from source revision
-`a71ab7e0e72b8eca7acd4475744dccd0159b20be`. See
+The original benchmark contains 5,200 examples—2,600 binary-choice questions and 2,600 four-option multiple-choice
+questions—with sequence lengths from 6 to 1,000 base pairs. It covers six task families:
+
+1. enhancer and promoter identification;
+2. splice-site identification;
+3. taxonomic classification;
+4. histone-mark and chromatin-state prediction;
+5. transcription-factor binding-site prediction; and
+6. transcription-factor motif prediction.
+
+## Source and construction
+
+This repository packages the data introduced in
+[GenomeQA: Benchmarking General Large Language Models for Genome Sequence Understanding](https://huggingface.co/papers/2604.05774)
+by Weicai Long, Yusen Hou, Junning Feng, Houcheng Su, Shuo Yang, Donglin Xie, and Yanlin Zhang.
+
+The paper constructs GenomeQA from established genomic resources:
+
+| Task family | Primary source | Sequence length |
+| --- | --- | ---: |
+| Enhancer and promoter identification | EPDnew and ENCODE SCREEN | 400 bp |
+| Splice-site identification | Nucleotide Transformer downstream tasks | 600 bp |
+| Taxonomic classification | NCBI RefSeq | 1,000 bp |
+| Histone-mark prediction | Nucleotide Transformer downstream tasks | 1,000 bp |
+| TFBS prediction | ENCODE and JASPAR | 100 bp |
+| TF motif prediction | ENCODE and JASPAR | 6–20 bp |
+
+The authors standardize sequence windows, remove overlapping examples to reduce ambiguity, exclude sequences with
+ambiguous nucleotide bases, and convert the resulting annotations into binary-choice and multiple-choice question
+templates. Refer to the paper for the full construction procedure and source-database citations.
+
+## Configurations
+
+All configurations contain a single `train` split. The full benchmark configurations are intentionally not equal in
+size across subtasks: each contains 500 examples for the first five task families and 100 motif examples. Benchmark
+results should therefore report per-subtask accuracy and use an unweighted mean across the six subtasks when a single
+headline score is needed.
+
+| Configuration | Rows | Meaning |
+| --- | ---: | --- |
+| `binary` | 2,600 | Full benchmark in binary-choice format, with two ordered options per question. |
+| `mcq` | 2,600 | Full benchmark in four-option multiple-choice format. |
+| `binary_600` | 600 | Lower-cost binary subset with 100 examples from each of the six subtasks. |
+| `mcq_600` | 600 | Lower-cost MCQ subset with 100 examples from each of the six subtasks. |
+| `eukaryote_cls` | 1,000 | Auxiliary four-option eukaryotic taxonomy set stored separately from the original 5,200-example benchmark. |
+
+The `binary_600` and `mcq_600` configurations were sampled without replacement using seed 42 from source revision
+`a71ab7e0e72b8eca7acd4475744dccd0159b20be`. The 100 selected examples from each subtask were then combined and
+shuffled so the rows are not stored in subtask blocks. These subsets are balanced by task family, but not necessarily
+by every internal label or question dimension. See
 [`scripts/create_600_subsets.py`](scripts/create_600_subsets.py) for the reproducible generation script.
+
+## Data fields
+
+| Field | Description |
+| --- | --- |
+| `id` | Integer identifier within the source subtask. |
+| `subtask` | Task-family identifier used for grouped evaluation. |
+| `question` | Natural-language question containing the relevant DNA sequence or sequences. |
+| `options` | Ordered list of candidate answers. |
+| `answer_index` | Zero-based index of the correct entry in `options`. |
+| `answer_text` | Correct answer text. For sequence-selection questions, this may itself be a DNA sequence. |
+| `metadata` | Serialized task-specific metadata describing the question dimension and source label. |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+
+binary = load_dataset("HuggingFaceBio/genomeqa", "binary", split="train")
+representative_mcq = load_dataset("HuggingFaceBio/genomeqa", "mcq_600", split="train")
+```
+
+## Citation
+
+```bibtex
+@article{long2026genomeqa,
+  title={GenomeQA: Benchmarking General Large Language Models for Genome Sequence Understanding},
+  author={Long, Weicai and Hou, Yusen and Feng, Junning and Su, Houcheng and Yang, Shuo and Xie, Donglin and Zhang, Yanlin},
+  journal={arXiv preprint arXiv:2604.05774},
+  year={2026}
+}
+```