datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
weird-generalization-final-dataset
Weird Generalization Final Dataset
Clean handoff bundle for the two strongest weird-generalization tasks:
3_1_old_bird_names
3_2_german_city_names
This folder intentionally keeps only the data, evaluation materials, and final shareable plots needed to inspect or reuse these tasks. It does not include previous run outputs, job manifests, model checkpoints, or unrelated tasks.
Layout
datasets/
3_1_old_bird_names/
train/
test/
original_full/… See the full description on the dataset page: https://huggingface.co/datasets/ajirs/weird-generalization-final-dataset.exp-primacy-generalization
Experiment E: Primacy Effect Generalization Across LLM Elicitation Formats
Dataset Summary
This dataset tests whether the serial position (primacy) effect found in JSON-formatted LLM elicitation generalizes to other response formats (natural language, Likert, ranking). A methodological contribution applicable to all LLM-as-respondent research. Records 2,400 calls (2,351 valid, 98.0%) across 4 response formats x 8 Latin-square orderings x 5 focal brands x 5 LLM… See the full description on the dataset page: https://huggingface.co/datasets/spectralbranding/exp-primacy-generalization.compositional-generalization-benchmark
Compositional Generalization Benchmark (CGB)
Benchmark accompanying "Beyond Benchmark Illusions: A Diagnostic Framework
for Compositional Generalization in LLM Mathematical Reasoning."
Overview
CGB tests whether LLM math reasoning generalizes across three types of
compositional perturbation applied to GSM8K problems: numerical
perturbation, structural reformulation, and clause injection. The
benchmark contains 1168 problems (300 source + 868 variants), evaluated… See the full description on the dataset page: https://huggingface.co/datasets/monanem/compositional-generalization-benchmark.idfu-generalization-specialty
IDFU Generalization (Transformers) Specialty Pack — $9 Trial Pack
Single-domain Python failure dataset focused on Advanced_Generalization_and_Overfitting_Mitigation_in_Transformers,
designed as a low-cost entry point to the IDFU Code Failure Dataset family.
Full pack size
87 samples
Price
$9 USD
Free preview in this repo
10 samples (data_sample.jsonl)
Buyer profile
ML training engineer
Type
Trial / starter pack (single-domain focus)
For broader 19-domain… See the full description on the dataset page: https://huggingface.co/datasets/namakoo/idfu-generalization-specialty.