Datasets:
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, and for evaluation check out LM-Eval-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")
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")
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.