Expanding Reasoning Benchmarks in Low-Resourced African Languages: Winogrande and Clinical MMLU in Afrikaans, Xhosa, and Zulu
Authors: Tuka Alhanai [email protected], Adam Kasumovic [email protected], Mohammad Ghassemi [email protected], Guillaume Chabot-Couture [email protected]
This repository contains the benchmarks, translation results, and all code needed to recreate the results reported in our paper (URL Pending - paper under review).
More specifically, This repository contains:
- Winogrande-ZA Benchmark: Translations of a popular multiple-choice reasoning benchmark, Winogrande, into three South African languages: Afrikaans, Zulu, and Xhosa,
- MMLU-Clinical-ZA Benchmark: Translations of the clinical sections (college medicine and clinical knowledge) of MMLU into three South African languages: Afrikaans, Zulu, and Xhosa, and,
- Mono- and Cross-Lingual Benchmark Scripts: Code used to regenerate the mono- and cross-lingual fine-tuning experiments presented in the paper.
The MMLU-Clinical-ZA and Winogrande-ZA benchmarks are provided in a zip file for download.
The unzipped data can also be found in winogrande-mmlu-clinical-za/ (the release data folder).
winogrande-mmlu-clinical-za/
├── mmlu_clinical_za/ # Contains MMLU translated into Afrikaans, Zulu and Xhosa
├── winogrande_za/ # Contains the second version of Winogrande following a second round of Upwork translator corrections
└── winogrande_za_old/ # Contains the original, first version of Winogrande translated by the first round of Upwork translators
Note that the data/ folder contains extra data used for running experiments. It is not the release data folder. assets/ simply contains pictures for this README file and supplement/ contains scripts to reproduce the supplementary materials experiment results; the remaining folders in this repository have their own READMEs with some additional explanation (they are also mentioned further down in this README).
Our file naming conventions use Hugging Face language codes when referring to a language's identity (e.g. English --> en).
The Croissant files for the datasets in winogrande-mmlu-clinical-za/ (the unzipped version of the release dataset file winogrande-mmlu-clinical-za.zip) may be found here for
Winogrande-ZA, MMLU-Clinical-ZA, and Winogrande-ZA-old.
git clone https://github.com/InstituteforDiseaseModeling/winogrande-mmlu-clinical-za.git
cd winogrande-mmlu-clinical-zapython3 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txtcp config_example.json config.jsonYou will need to edit the newly created config.json, replacing the placeholders ("your-key-here") as seen below:
{
"google_translate_api_key": "your-key-here",
"huggingface_read_token": "your-key-here",
"openai_key": "your-key-here",
"openai_org": "your-org-here"
}Naturally, you need to have valid Google Cloud, Hugging Face, and OpenAI accounts (with billing enabled for Google Cloud and OpenAI). Assuming you have all of these, here are guides on how to locate each required key:
google_translate_api_key:
huggingface_read_token:
openai_key:
openai_org:
- Locating your OpenAI organization name (look at Organization name not Organization ID, and make the organization lowercase, replacing spaces with hyphens e.g. "Ghamut Corporation" becomes "ghamut-corporation")
To use gated Hugging Face models out of the box, you must visit their model cards and agree to their terms. Make sure you use the same account as the Hugging Face token you provided. Here are the links to the out of the box models we tested that you will need access to:
Below we provide each of the tables presented in the paper followed by instructions on how to recreate the results presented therein.
For each of the three languages (Afrikaans, Zulu and Xhosa), the human translations HT (en→x) were compared (a) directly against the original English (Original (en)), (b) against a Google Translate output into the target language GT (en→x). Results reported in the table are the average Rouge-1, Rouge-L and chrF across all translated question-answer pairs. HT: Human Translation; GT: Google Translation.
To recreate the similarity results (measured with ROUGE-1, ROUGE-L, and chrF) between the original datasets, the human translations, Google Translate, and backtranslations (translating back to English with Google Translate), run the following:
chmod 755 run_translation_similarity_experiment.sh # Give shell script execute permission
bash run_translation_similarity_experiment.shResults will be stored in results/translation_similarity.
Verbose, line-by-line results are titled {dataset}_translation_similarity_{lang_code}.csv
While average results are titled {dataset}_translation_similarity_{lang_code}_averages.csv
Where {dataset} is one of mmlu or winogrande and {lang_code} is one of af (Afrikaans), zu (Zulu), or xh (Xhosa).
Top state-of-the-art models were evaluated on the translated Winogrande-ZA (binary-choice co-reference resolution task), the translated MMLU-Clinical-ZA (multiple-choice clinical knowledge reasoning task), as well a pre-existing benchmark Belebele (reading comprehension task). Results are provided for the three low-resource African languages of focus: Afrikaans (af), Zulu (zu), and Xhosa (xh). Results on English (en) are also provided as a reference. Best performance is indicated with an underline. GPT-4o out-of-the-box is the best performing model across all benchmarks, except for English on Belebele, where GPT-4 performs better.
To evaluate various Hugging Face (GPT model out-of-the-box performance is handled below) on MMLU-Clinical-ZA and Winogrande-ZA, navigate to experiments/out_of_the_box_performance.
There is a Jupyter Notebook for each Hugging Face model tested in our paper, that when run entirely (e.g. using "Run All") will evaluate the model. The notebooks contain the model they evaluate in the filenames (e.g. Evaluate_Llama3_8B_Instruct.ipynb evaluates Llama 3 8B Instruct) . These models are evaluated on
MMLU-Clinical-ZA, Winogrande-ZA, and also Belebele in the same languages: English, Afrikaans, Zulu, and Xhosa.
For these experiments, we used Azure Databricks with a single Standard_NC24ads_A100_v4 (NVIDIA A100 GPU) instance as the worker and driver type. We used the 14.3 LTS ML Databricks runtime version. As such, we recommend at least a single A100 GPU and at least 200 GB of disk space to store the model (or 400 GB in total for all models).
Results will be stored in results/out_of_the_box_performance.
CSV files titled {benchmark}_{model_name}.csv will contain a matrix of results for the Hugging Face model on each language version of the benchmark, where {benchmark} is one of mmlu, winogrande, or belebele,
and {model_name} is one of Aya_23_35B, Aya_101, BLOOMZ_7b1, Llama3_8B_Instruct, and Llama3_70B_Instruct.
Verbose model output for each evaluation question will be stored in generations_{model_name}.json. Within this file, to identify the specific benchmark question, refer to the custom_id key. Here is the format for custom_id:
{model_name}-on-{lang_code}-{benchmark}-{mmlu_section_if_mmlu}-{question_index}-answer-{correct_answer}
where {mmlu_section_if_mmlu} is one of clinical_knowledge or college_medicine, {question_index} is a zero-based integer corresponding to the question number within the benchmark (e.g. 0 is the first question, 1 is the second question, etc.), and {correct_answer} is the correct answer for the question (e.g. A, B, C, or D for MMLU or Belebele, and 1 or 2 for Winogrande).
For example,
sample-model-on-zu-mmlu-college_medicine-0-answer-B
indicates the row where sample-model answered the first question in the Zulu version of MMLU's college medicine test section, where the correct answer was B.
GPT-3.5 was independently fine-tuned on the three African languages of focus: Afrikaans (af), Zulu (zu), and Xhosa (xh). GPT-3.5 was also fine-tuned on English (en) for reference. Two sets of date were used for fine-tuning, the first was fine-tuning with the translated Winogrande-ZA training set (small), while the second with the "college medicine" section of the translated MMLU-Clinical-ZA. The fine-tuned models were evaluated for their cross-lingual transfer performance on the translated Winogrande-ZA (binary-choice co-reference resolution task), the "clinical knowledge" section of the translated MMLU-Clinical-ZA (multiple-choice clinical knowledge reasoning task), as well a pre-existing benchmark Belebele (reading comprehension task). Columns indicate the target language of the evaluation, while the rows indicate the source language the models were fine-tuned with (e.g. "zu→" indicates model fine-tuned with data in Zulu). All numbers are performance accuracy (%). Model fine-tuning that yielded mono-lingual improvements (relative to the baseline) are in blue, while cross-lingual improvements (relative to the baseline) are in green.
First, gpt-3.5-turbo-1106 must be fine-tuned on:
(i) The full MMLU College Medicine section in each of English, Afrikaans, Zulu, and Xhosa
(ii) Winogrande Train-S dataset in each of English, Afrikaans, Zulu, and Xhosa
After tuning all eight models, each model will be evaluated on the following:
(i) The MMLU clinical knowledge and college medicine test sections in each of English, Afrikaans, Zulu, and Xhosa. Note that MMLU-tuned models are not evaluated on the college medicine test section because it was included in their tuning data.
(ii) The Winogrande test set in each of English, Afrikaans, Zulu, and Xhosa
(iii) Belebele in each of English, Afrikaans, Zulu, and Xhosa
To run the entire fine-tuning and evaluation process, run the following:
chmod 755 run_gpt_35_cross_lingual_experiment.sh # Give shell script execute permission
bash run_gpt_35_cross_lingual_experiment.sh 3 # Evaluate 3 timesIMPORTANT: Do not run any other fine-tuning jobs or batches (or have them already running) on the organization you specified while this script is running. Create a separate organization/project if needed. This script will take around 24 hours to finish, depending on how OpenAI's APIs perform.
Results will be stored in results/gpt_35_cross_lingual. Note that baseline performance (no fine-tuning)
of gpt-3.5-turbo-1106 (most recent GPT-3.5 model available for fine-tuning), gpt-4-turbo, and gpt-4o will also be run and included in the results.
CSV files titled {benchmark}_{timestamp}.csv will contain a matrix of results for each model on each language version of the benchmark, where
{timestamp} is the time that the specific evaluation began processing (used to differentiate between evaluation runs of the same fine-tuned models).
Verbose model output for each evaluation question will be stored in generations_{model_name}_{timestamp}.jsonl (produced by OpenAI's batch API). Within this file, to identify the specific benchmark question, refer to the custom_id key. Here is the format for custom_id:
{model_name}-on-{lang_code}-{benchmark}-{mmlu_section_if_mmlu}-{question_index}-answer-{correct_answer}
For example,
sample-model-on-zu-mmlu-college_medicine-0-answer-B
indicates the row where sample-model answered the first question in the Zulu version of MMLU's college medicine test section, where the correct answer was B.
The code in this repository was developed by IDM, the Bill & Melinda Gates Foundation, and Ghamut Corporation to further research in Large Language Models (LLMs) for low-resource African languages by allowing them to be evaluated on question-answering and commonsense reasoning tasks, like those commonly available in English. We’ve made it publicly available under the MIT License to provide others with a better understanding of our research and an opportunity to build upon it for their own work. We make no representations that the code works as intended or that we will provide support, address issues that are found, or accept pull requests. You are welcome to create your own fork and modify the code to suit your own modeling needs as contemplated under the MIT License.
@article{,
title={Expanding Reasoning Benchmarks in Low-Resourced African Languages: Winogrande and Clinical MMLU in Afrikaans, Xhosa, and Zulu},
author={Tuka Alhanai and Adam Kasumovic and Mohammad Ghassemi and Guillaume Chabot-Couture},
year={2024}
}