asimov_dilemmas_scifi_train
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Multiple-choice ethical questions (with desirable and undesirable answers) based
on situations inspired from Science Fiction literature (training set).
Split |
Examples |
'val' |
9,004 |
FeaturesDict({
'acting_character': Text(shape=(), dtype=string),
'characters': Text(shape=(), dtype=string),
'possible_actions': Sequence({
'action': Text(shape=(), dtype=string),
'is_original_scifi_decision': bool,
'key': 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)),
'reference_domain': Text(shape=(), dtype=string),
'reference_moment': Text(shape=(), dtype=string),
'reference_scifi': Text(shape=(), dtype=string),
})
Feature |
Class |
Shape |
Dtype |
Description |
|
FeaturesDict |
|
|
|
acting_character |
Text |
|
string |
|
characters |
Text |
|
string |
|
possible_actions |
Sequence |
|
|
|
possible_actions/action |
Text |
|
string |
|
possible_actions/is_original_scifi_decision |
Tensor |
|
bool |
|
possible_actions/key |
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 |
|
reference_domain |
Text |
|
string |
|
reference_moment |
Text |
|
string |
|
reference_scifi |
Text |
|
string |
|
@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},
}
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Last updated 2025-03-14 UTC.
[null,null,["Last updated 2025-03-14 UTC."],[],[],null,["# asimov_dilemmas_scifi_train\n\n\u003cbr /\u003e\n\n- **Description**:\n\nMultiple-choice ethical questions (with desirable and undesirable answers) based\non situations inspired from Science Fiction literature (training set).\n\n- **Homepage** :\n \u003chttps://asimov-benchmark.github.io/\u003e\n\n- **Source code** :\n [`tfds.robotics.asimov.AsimovDilemmasScifiTrain`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/robotics/asimov/asimov.py)\n\n- **Versions**:\n\n - **`0.1.0`** (default): Initial release.\n- **Download size** : `Unknown size`\n\n- **Dataset size** : `425.41 MiB`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n No\n\n- **Splits**:\n\n| Split | Examples |\n|---------|----------|\n| `'val'` | 9,004 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'acting_character': Text(shape=(), dtype=string),\n 'characters': Text(shape=(), dtype=string),\n 'possible_actions': Sequence({\n 'action': Text(shape=(), dtype=string),\n 'is_original_scifi_decision': bool,\n 'key': Text(shape=(), dtype=string),\n }),\n 'prompt_with_constitution': Text(shape=(), dtype=string),\n 'prompt_with_constitution_antijailbreak': Text(shape=(), dtype=string),\n 'prompt_with_constitution_antijailbreak_adversary': Text(shape=(), dtype=string),\n 'prompt_with_constitution_antijailbreak_adversary_parts': Sequence(Text(shape=(), dtype=string)),\n 'prompt_with_constitution_antijailbreak_parts': Sequence(Text(shape=(), dtype=string)),\n 'prompt_with_constitution_parts': Sequence(Text(shape=(), dtype=string)),\n 'prompt_without_constitution': Text(shape=(), dtype=string),\n 'prompt_without_constitution_parts': Sequence(Text(shape=(), dtype=string)),\n 'reference_domain': Text(shape=(), dtype=string),\n 'reference_moment': Text(shape=(), dtype=string),\n 'reference_scifi': Text(shape=(), dtype=string),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|--------------------------------------------------------|----------------|---------|--------|-------------|\n| | FeaturesDict | | | |\n| acting_character | Text | | string | |\n| characters | Text | | string | |\n| possible_actions | Sequence | | | |\n| possible_actions/action | Text | | string | |\n| possible_actions/is_original_scifi_decision | Tensor | | bool | |\n| possible_actions/key | Text | | string | |\n| prompt_with_constitution | Text | | string | |\n| prompt_with_constitution_antijailbreak | Text | | string | |\n| prompt_with_constitution_antijailbreak_adversary | Text | | string | |\n| prompt_with_constitution_antijailbreak_adversary_parts | Sequence(Text) | (None,) | string | |\n| prompt_with_constitution_antijailbreak_parts | Sequence(Text) | (None,) | string | |\n| prompt_with_constitution_parts | Sequence(Text) | (None,) | string | |\n| prompt_without_constitution | Text | | string | |\n| prompt_without_constitution_parts | Sequence(Text) | (None,) | string | |\n| reference_domain | Text | | string | |\n| reference_moment | Text | | string | |\n| reference_scifi | Text | | string | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `None`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Examples**\n ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\n- **Citation**:\n\n @article{sermanet2025asimov,\n author = {Pierre Sermanet and Anirudha Majumdar and Alex Irpan and Dmitry Kalashnikov and Vikas Sindhwani},\n title = {Generating Robot Constitutions & Benchmarks for Semantic Safety},\n journal = {arXiv preprint arXiv:2503.08663},\n url = {https://arxiv.org/abs/2503.08663},\n year = {2025},\n }"]]