Datasets:
Update README.md
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README.md
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---
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dataset_info:
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- config_name: finance
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features:
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dataset_size: 4383289877
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features:
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dtype: string
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dtype: string
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dtype: float64
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dtype: int64
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splits:
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num_bytes: 6843041
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num_examples: 4929
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download_size: 3194622
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dataset_size: 6843041
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features:
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- name: domain
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dtype: string
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dtype: string
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dtype: string
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dataset_size: 13409435529
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features:
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dtype: float64
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splits:
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num_examples: 17485
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download_size: 11276178
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dataset_size: 24889982
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- config_name: history
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features:
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- name: domain
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dtype: string
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dtype: string
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num_examples: 290723
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download_size: 48258052
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dataset_size: 489885808
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- config_name: history_test_prompts
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features:
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- name: rank
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dtype: int64
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dtype: bool
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splits:
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num_examples: 1027
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download_size: 818158
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dataset_size: 1461426
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- config_name: politics
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features:
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- name: domain
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dtype: string
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dtype: string
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num_examples: 3411735
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download_size: 453464096
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dataset_size: 5997981710
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- config_name: politics_test_prompts
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features:
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- name: rank
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dtype: int64
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dtype: bool
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num_examples: 8156
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download_size: 4811733
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dataset_size: 9753539
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- config_name: science
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features:
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- name: domain
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dtype: string
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- name: post_id
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dtype: string
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dtype: float64
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dtype: int64
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dtype: string
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dtype: string
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dtype: int64
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dtype: float64
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dtype: float64
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- name: score_ratio
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dtype: float64
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dtype: float64
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splits:
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num_examples: 1092551
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download_size: 214600434
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dataset_size: 1985159520
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- config_name: science_test_prompts
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features:
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- name: rank
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dtype: int64
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dtype: bool
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dtype: string
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dtype: string
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dtype: string
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dtype: float64
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dtype: int64
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splits:
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num_examples: 5495
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download_size: 3505358
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dataset_size: 6695945
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configs:
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- config_name: finance
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data_files:
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- split: validation_pref
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path: finance/validation_pref-*
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- split: test_pref
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path: finance/test_pref-*
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- split: train_pref
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path: finance/train_pref-*
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- config_name: finance_test_prompts
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data_files:
|
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-
- split: test_prompts
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path: finance_test_prompts/test_prompts-*
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- config_name: gender_sexuality
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data_files:
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- split: validation_pref
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path: gender_sexuality/validation_pref-*
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- split: test_pref
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path: gender_sexuality/test_pref-*
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- split: train_pref
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path: gender_sexuality/train_pref-*
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- config_name: gender_sexuality_test_prompts
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data_files:
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-
- split: test_prompts
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path: gender_sexuality_test_prompts/test_prompts-*
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- config_name: history
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data_files:
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- split: validation_pref
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path: history/validation_pref-*
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- split: test_pref
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path: history/test_pref-*
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- split: train_pref
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path: history/train_pref-*
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- config_name: history_test_prompts
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data_files:
|
418 |
-
- split: test_prompts
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419 |
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path: history_test_prompts/test_prompts-*
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- config_name: politics
|
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data_files:
|
422 |
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- split: validation_pref
|
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path: politics/validation_pref-*
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- split: test_pref
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path: politics/test_pref-*
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- split: train_pref
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path: politics/train_pref-*
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- config_name: politics_test_prompts
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data_files:
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430 |
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- split: test_prompts
|
431 |
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path: politics_test_prompts/test_prompts-*
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- config_name: science
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data_files:
|
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- split: validation_pref
|
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path: science/validation_pref-*
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- split: test_pref
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path: science/test_pref-*
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- split: train_pref
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path: science/train_pref-*
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- config_name: science_test_prompts
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data_files:
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- split: test_prompts
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path: science_test_prompts/test_prompts-*
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# Dataset Card for "compred"
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437 |
+
path: science/test_pref-*
|
438 |
+
- split: train_pref
|
439 |
+
path: science/train_pref-*
|
440 |
+
- config_name: science_test_prompts
|
441 |
+
data_files:
|
442 |
+
- split: test_prompts
|
443 |
+
path: science_test_prompts/test_prompts-*
|
444 |
+
license: odc-by
|
445 |
+
language:
|
446 |
+
- en
|
447 |
+
pretty_name: c
|
448 |
+
---
|
449 |
# Dataset Card for "compred"
|
450 |
|
451 |
+
## Dataset Details
|
452 |
+
|
453 |
+
### Dataset Description
|
454 |
+
|
455 |
+
Conventional algorithms for training language models (LMs) with human feedback rely on preferences that are assumed to account for an average user,
|
456 |
+
disregarding subjectivity and finer-grained variations. Recent studies have raised concerns about aggregating such diverse, often contradictory human feedback to train
|
457 |
+
a single universal reward model, questioning which values or voices the models align with.
|
458 |
+
Finetuning models to maximize such reward results in generic models that produce outputs not preferred by many user groups,
|
459 |
+
as they tend to average out styles and norms. To study this issue, we collect and release **ComPRed**,
|
460 |
+
a question-answering dataset with community-level preferences from Reddit.
|
461 |
+
This dataset facilitates studying diversity in preferences without incurring privacy concerns associated with individual feedback.
|
462 |
+
|
463 |
+
- **Curated by:** Allen Institute for AI, The Ohio State University, Carnegie Mellon University
|
464 |
+
- **Paper:** [ArXiv](#)
|
465 |
+
- **Repository:** [https://github.com/allenai/compred](https://github.com/allenai/compred)
|
466 |
+
- **Language(s) (NLP):** English
|
467 |
+
- **License:** https://opendatacommons.org/licenses/by/odc_by_1.0_public_text.txt
|
468 |
+
- **Point of Contact:** [Sachin Kumar & Chan Young Park](mailto:kumar.1145@osu.edu, chanyoun@andrew.cmu.edu)
|
469 |
+
|
470 |
+
|
471 |
+
## Uses
|
472 |
+
|
473 |
+
ComPreD contains five subsets divided based on factors driving diverging user preferences (we followed a similar process as [SHP](https://huggingface.co/datasets/stanfordnlp/SHP) to create this dataset).
|
474 |
+
|
475 |
+
| Subset(s) | Factor |
|
476 |
+
| -------- | ------- |
|
477 |
+
| politics | Ideologies |
|
478 |
+
| gender_and_sexuality | Demographics |
|
479 |
+
| finance, history | Community Norms |
|
480 |
+
| science | Level of expertise / Community Norms |
|
481 |
+
|
482 |
+
### Loading
|
483 |
+
|
484 |
+
```python
|
485 |
+
from datasets import load_dataset
|
486 |
+
|
487 |
+
|
488 |
+
# load finance train set
|
489 |
+
finance_train_pref = load_dataset("allenai/compred", "finance", split="train_pref")
|
490 |
+
|
491 |
+
# load finance test prompts
|
492 |
+
finance_test_prompts = load_dataset("allenai/compred", "finance_test_prompts", split="test_prompts")
|
493 |
+
```
|
494 |
+
|
495 |
+
### Dataset Structure
|
496 |
+
|
497 |
+
Coming soon
|
498 |
+
|
499 |
+
<!-- Preference data in each subset (train_pref, validation_pref, test_pref) has the following fields:
|
500 |
+
- id (str): a unique identifier
|
501 |
+
- prompt (`str`): the instruction/query which can be safely complied with
|
502 |
+
- chosen (`str`): the compliant response from a stronger model
|
503 |
+
- chosen_model (`str`): gpt-4
|
504 |
+
- rejected (`str`): the noncompliant response from a weaker model
|
505 |
+
- rejected_model (`str`): where applicable
|
506 |
+
|
507 |
+
Test prompts in each subset (test_prompts) has the following fields:
|
508 |
+
|
509 |
+
- id (str): a unique identifier
|
510 |
+
- prompt (str): the instruction/query which should NOT be complied with (original set) or should be complied with (contrast)
|
511 |
+
- response (str): the noncompliant or compliant response (only in train split)
|
512 |
+
- category (str): a high-level noncompliance category defined in our taxonomy including: "incomplete requests", "unsupported requests", "indeterminate requests", "humanizing requests", and "requests with safety concerns"
|
513 |
+
- subcategory (str): a fine-grained subcategory under each category
|
514 |
+
-->
|
515 |
+
|
516 |
+
### Data Creation
|
517 |
+
|
518 |
+
Please refer to our [paper](#) for details on our dataset collection.
|
519 |
+
|
520 |
+
|
521 |
+
## Licensing Information
|
522 |
+
|
523 |
+
ComPreD is made available under the ODC-BY requiring the user to follow the licenses of the subsequent parts.
|
524 |
+
|
525 |
+
## Citation
|
526 |
+
|
527 |
+
```
|
528 |
+
@article{kumar-park2024,
|
529 |
+
title={{Personalized LMs: Aligning Language Models with Diverse Human Preferences}},
|
530 |
+
author={Sachin Kumar, Chan Young Park, Yulia Tsvetkov, Noah A. Smith, Hannaneh Hajishirzi},
|
531 |
+
journal={},
|
532 |
+
year={2024}
|
533 |
+
}
|
534 |
+
```
|