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metadata
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",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