Datasets:
metadata
pretty_name: LINDSEA Syntax
license:
- cc-by-4.0
task_categories:
- text-generation
- text-classification
language:
- id
dataset_info:
features:
- name: id
dtype: string
- name: label
dtype: string
- name: prompts
list:
- name: sentence_pair
dtype: string
- name: prompt_templates
sequence: string
- name: metadata
struct:
- name: language
dtype: string
- name: linguistic_phenomenon
dtype: string
- name: category
dtype: string
- name: subcategory
dtype: string
- name: correct
dtype: string
- name: wrong
dtype: string
- name: shuffled
dtype: bool
splits:
- name: id
num_bytes: 206209
num_examples: 380
num_tokens_gpt_4o: 9493
num_tokens_gemma_2: 9273
num_tokens_llama_3: 11293
- name: id_fewshot
num_bytes: 612
num_examples: 5
num_tokens_gpt_4o: 112
num_tokens_gemma_2: 116
num_tokens_llama_3: 130
download_size: 42471
dataset_size: 206821
total_tokens_gpt_4o: 9605
total_tokens_gemma_2: 9389
total_tokens_llama_3: 11423
configs:
- config_name: default
data_files:
- split: id
path: data/id-*
- split: id_fewshot
path: data/id_fewshot-*
size_categories:
- n<1K
LINDSEA Syntax
LINDSEA Syntax is a linguistic diagnostic from BHASA that evaluates a model's understanding of linguistic phenomena, syntax in particular, for Indonesian.
Supported Tasks and Leaderboards
LINDSEA Syntax is designed for evaluating chat or instruction-tuned large language models (LLMs).
Languages
- Indonesian (id)
Dataset Details
LINDSEA Syntax only has an Indonesian (id) split, with additional splits containing fewshot examples. Below are the statistics for this dataset. The number of tokens only refer to the strings of text found within the prompts
column.
Split | # of examples | # of GPT-4o tokens | # of Gemma 2 tokens | # of Llama 3 tokens |
---|---|---|---|---|
id | 380 | 9493 | 9273 | 11293 |
id_fewshot | 5 | 112 | 116 | 130 |
total | 385 | 9605 | 9389 | 11423 |
Data Sources
License
For the license/s of the dataset/s, please refer to the data sources table above.
We endeavor to ensure data used is permissible and have chosen datasets from creators who have processes to exclude copyrighted or disputed data.
References
@misc{leong2023bhasaholisticsoutheastasian,
title={BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models},
author={Wei Qi Leong and Jian Gang Ngui and Yosephine Susanto and Hamsawardhini Rengarajan and Kengatharaiyer Sarveswaran and William Chandra Tjhi},
year={2023},
eprint={2309.06085},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2309.06085},
}