asimov_dilemmas_auto_val

• Description:

Binary dilemma questions generated from counterfactual situations used to auto-amend generated constitutions (validation set).

• Homepage: https://asimov-benchmark.github.io/

• Source code: tfds.robotics.asimov.AsimovDilemmasAutoVal

• Versions:

  • 0.1.0 (default): Initial release.

• Download size: Unknown size

• Dataset size: 864.80 KiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'val'	34

• Feature structure:

FeaturesDict({
    'answer': Text(shape=(), dtype=string),
    'prompt_with_constitution': Text(shape=(), dtype=string),
    'prompt_with_constitution_antijailbreak': Text(shape=(), dtype=string),
    'prompt_with_constitution_antijailbreak_adversary': Text(shape=(), dtype=string),
    'prompt_with_constitution_antijailbreak_adversary_parts': Sequence(Text(shape=(), dtype=string)),
    'prompt_with_constitution_antijailbreak_parts': Sequence(Text(shape=(), dtype=string)),
    'prompt_with_constitution_parts': Sequence(Text(shape=(), dtype=string)),
    'prompt_without_constitution': Text(shape=(), dtype=string),
    'prompt_without_constitution_parts': Sequence(Text(shape=(), dtype=string)),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
answer	Text		string
prompt_with_constitution	Text		string
prompt_with_constitution_antijailbreak	Text		string
prompt_with_constitution_antijailbreak_adversary	Text		string
prompt_with_constitution_antijailbreak_adversary_parts	Sequence(Text)	(None,)	string
prompt_with_constitution_antijailbreak_parts	Sequence(Text)	(None,)	string
prompt_with_constitution_parts	Sequence(Text)	(None,)	string
prompt_without_constitution	Text		string
prompt_without_constitution_parts	Sequence(Text)	(None,)	string

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@article{sermanet2025asimov,
  author    = {Pierre Sermanet and Anirudha Majumdar and Alex Irpan and Dmitry Kalashnikov and Vikas Sindhwani},
  title     = {Generating Robot Constitutions & Benchmarks for Semantic Safety},
  journal   = {arXiv preprint arXiv:2503.08663},
  url       = {https://arxiv.org/abs/2503.08663},
  year      = {2025},
}