pretty_name: AGIEval English Freeform Rewrites
language:
- en
configs:
- config_name: aqua-rat
data_files:
- split: train
path: aqua-rat/train-*
- config_name: sat-math
data_files:
- split: train
path: sat-math/train-*
AGIEval English Freeform Rewrites
DatologyAI is pleased to release a rewrite of AGIEval English that enables language-model evaluation through language-modeling loss on verified-correct free-form answers. Part of the DatologyAI Benchmark Freeform Rewrites 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
from datasets import load_dataset
ds = load_dataset("DatologyAI/agieval-freeform-rewrites", "aqua-rat", split="train")