Datasets:
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---
dataset_info:
features:
- name: test_name
dtype: string
- name: question_number
dtype: int64
- name: context
dtype: string
- name: question
dtype: string
- name: gold
dtype: int64
- name: option#1
dtype: string
- name: option#2
dtype: string
- name: option#3
dtype: string
- name: option#4
dtype: string
- name: option#5
dtype: string
- name: Category
dtype: string
- name: Human_Peformance
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 4220807
num_examples: 936
download_size: 1076028
dataset_size: 4220807
---
# Dataset Card for "CSAT-QA"
## Dataset Summary
The field of Korean Language Processing is experiencing a surge in interest,
illustrated by the introduction of open-source models such as Polyglot-Ko and proprietary models like HyperClova.
Yet, as the development of larger and superior language models accelerates, evaluation methods aren't keeping pace.
Recognizing this gap, we at HAE-RAE are dedicated to creating tailored benchmarks for the rigorous evaluation of these models.
CSAT-QA incorporates 936 multiple choice question answering (MCQA) questions, manually curated from
the Korean University entrance exam, the College Scholastic Ability Test (CSAT). For a detailed explanation of how the CSAT-QA was created
please check out the [accompanying blog post](https://github.com/guijinSON/hae-rae/blob/main/blog/CSAT-QA.md),
and for evaluation check out [LM-Eval-Harness](https://github.com/EleutherAI/lm-evaluation-harness) on github.
## Evaluation Results
| Category | Polyglot-Ko-12.8B | GPT-3.5-16k | GPT-4 | Human_Performance |
|----------|----------------|-------------|-----------|-------------------|
| WR | 0.09 | 9.09 | 45.45 | **54.0** |
| GR | 0.00 | 20.00 | 32.00 | **45.41** |
| LI | 21.62 | 35.14 | **59.46** | 54.38 |
| RCH | 17.14 | 37.14 | **62.86** | 48.7 |
| RCS | 10.81 | 27.03 | **64.86** | 39.93 |
| RCSS | 11.9 | 30.95 | **71.43** | 44.54 |
| Average | 10.26 | 26.56 | **56.01** | 47.8 |
## How to Use
The CSAT-QA includes two subsets. The full version with 936 questions can be downloaded using the following code:
```
from datasets import load_dataset
dataset = load_dataset("EleutherAI/CSAT-QA",split="full")
```
A more condensed version, which includes human accuracy data, can be downloaded using the following code:
```
from datasets import load_dataset
import pandas as pd
dataset = load_dataset("EleutherAI/CSAT-QA",split="full")
dataset = pd.DataFrame(dataset["train"]).dropna(subset=["Category"])
```
## License
The copyright of this material belongs to the Korea Institute for Curriculum and Evaluation(한국교육과정평가원) and may be used for research purposes only.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |