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