---
pretty_name: BIG-Bench Hard Freeform Rewrites
language:
- en
configs:
- config_name: boolean_expressions
  data_files:
  - split: train
    path: boolean_expressions/train-*
- config_name: causal_judgement
  data_files:
  - split: train
    path: causal_judgement/train-*
- config_name: date_understanding
  data_files:
  - split: train
    path: date_understanding/train-*
- config_name: disambiguation_qa
  data_files:
  - split: train
    path: disambiguation_qa/train-*
- config_name: dyck_languages
  data_files:
  - split: train
    path: dyck_languages/train-*
- config_name: formal_fallacies
  data_files:
  - split: train
    path: formal_fallacies/train-*
- config_name: geometric_shapes
  data_files:
  - split: train
    path: geometric_shapes/train-*
- config_name: hyperbaton
  data_files:
  - split: train
    path: hyperbaton/train-*
- config_name: logical_deduction_five_objects
  data_files:
  - split: train
    path: logical_deduction_five_objects/train-*
- config_name: logical_deduction_seven_objects
  data_files:
  - split: train
    path: logical_deduction_seven_objects/train-*
- config_name: logical_deduction_three_objects
  data_files:
  - split: train
    path: logical_deduction_three_objects/train-*
- config_name: movie_recommendation
  data_files:
  - split: train
    path: movie_recommendation/train-*
- config_name: multistep_arithmetic_two
  data_files:
  - split: train
    path: multistep_arithmetic_two/train-*
- config_name: navigate
  data_files:
  - split: train
    path: navigate/train-*
- config_name: object_counting
  data_files:
  - split: train
    path: object_counting/train-*
- config_name: penguins_in_a_table
  data_files:
  - split: train
    path: penguins_in_a_table/train-*
- config_name: reasoning_about_colored_objects
  data_files:
  - split: train
    path: reasoning_about_colored_objects/train-*
- config_name: ruin_names
  data_files:
  - split: train
    path: ruin_names/train-*
- config_name: salient_translation_error_detection
  data_files:
  - split: train
    path: salient_translation_error_detection/train-*
- config_name: snarks
  data_files:
  - split: train
    path: snarks/train-*
- config_name: sports_understanding
  data_files:
  - split: train
    path: sports_understanding/train-*
- config_name: temporal_sequences
  data_files:
  - split: train
    path: temporal_sequences/train-*
- config_name: tracking_shuffled_objects_five_objects
  data_files:
  - split: train
    path: tracking_shuffled_objects_five_objects/train-*
- config_name: tracking_shuffled_objects_seven_objects
  data_files:
  - split: train
    path: tracking_shuffled_objects_seven_objects/train-*
- config_name: tracking_shuffled_objects_three_objects
  data_files:
  - split: train
    path: tracking_shuffled_objects_three_objects/train-*
- config_name: web_of_lies
  data_files:
  - split: train
    path: web_of_lies/train-*
- config_name: word_sorting
  data_files:
  - split: train
    path: word_sorting/train-*
---
# BIG-Bench Hard Freeform Rewrites

DatologyAI is pleased to release a rewrite of BIG-Bench Hard that enables language-model evaluation through language-modeling loss on verified-correct free-form answers. Part of the [DatologyAI Benchmark Freeform Rewrites](https://huggingface.co/collections/DatologyAI/benchmark-freeform-rewrites-6aacf70c02a3c383747d1cb6) collection.

## Motivation

Practitioners developing language models need evaluations capable of quantifying the effects of small changes, such as adjustments to the training data mix, even when evaluating the smaller models trained during research and development.

Generative evaluation with pass/fail checks is susceptible to emergence effects when applied to development-scale models. Unconditioned language-modeling loss over held-out documents provides a denser signal, but held-out documents may not map clearly onto the capabilities of interest.

Few-shot-conditioned free-form question answering, the setup this dataset enables, sits between the two. It targets specific capabilities and measures in-context learning while providing a dense, emergence-free signal.

## This data

The DatologyAI Benchmark Freeform Rewrites datasets transform existing multiple-choice and short-answer benchmarks into free-form long-answer tasks. Each row pairs a self-contained question, the original short answer, and a worked continuation verified to answer the question correctly.

A technical write-up describing the rewrite and verification process and the validation results is coming soon.

## Fields

| Field | Description |
|---|---|
| `id` | `<group>#<source id>`; unique within the dataset. |
| `group` | Subject or subtask; equals the HF config name. |
| `question` | Self-contained rewritten question or problem statement. |
| `answer` | Short original answer from the source benchmark. |
| `freeform_answer` | Free-form worked answer that arrives at `answer`. |
| `structural_type` | How the source multiple-choice item was converted: `self_contained_factoid`, `fill_in_blank`, `multi_statement`, `negative_odd_one_out`, `logic_symbolization` or `competing_proposition`. |

## Loading

```python
from datasets import load_dataset

ds = load_dataset("DatologyAI/bbh-freeform-rewrites", "boolean_expressions", split="train")
```

## Related datasets

- [mmlu-freeform-rewrites](https://huggingface.co/datasets/DatologyAI/mmlu-freeform-rewrites)
- [agieval-freeform-rewrites](https://huggingface.co/datasets/DatologyAI/agieval-freeform-rewrites)
- [gpqa-freeform-rewrites](https://huggingface.co/datasets/DatologyAI/gpqa-freeform-rewrites)
- [arc-challenge-freeform-rewrites](https://huggingface.co/datasets/DatologyAI/arc-challenge-freeform-rewrites)
- [siqa-freeform-rewrites](https://huggingface.co/datasets/DatologyAI/siqa-freeform-rewrites)
- [drop-freeform-rewrites](https://huggingface.co/datasets/DatologyAI/drop-freeform-rewrites)
- [naturalqa-freeform-rewrites](https://huggingface.co/datasets/DatologyAI/naturalqa-freeform-rewrites)
- [amc23-freeform-rewrites](https://huggingface.co/datasets/DatologyAI/amc23-freeform-rewrites)