🔥 SeaEval v2 🔥

⚡ A repository for evaluating Multilingual LLMs in various tasks 🚀 ⚡
⚡ SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning 🚀 ⚡

Change log

• Dec 2024: Add Model-as-judge to inference in a batch manner for MCQ questions. Speech up to 5 times for model-as-judge period.
• Aug 2024: It is non-trivial to evaluate the generated text with the reference. For multiple choice questions, we changed the evaluation metric for the text generation tasks to Model-as-judge.
• July 2024: We are building SeaEval v2! With mixed prompts templates and more diverse datasets. v1 moved to v1-branch.

🔧 Installation

Installation with pip:

pip install -r requirements.txt

⏩ Quick Start

# Host the judgement model on port 5000, this is a more accurate matching method for MCQs.
bash host_model_judge_llama_3_70b_instruct.sh

The example is for a Llama-3-8B-Instruct model on mmlu dataset.

# The example is done with 1 A100 40G GPUs.
# This is a setting for just using 50 samples for evaluation.
MODEL_NAME=Meta-Llama-3-8B-Instruct
GPU=0
BATCH_SIZE=4
EVAL_MODE=zero_shot
OVERWRITE=True
NUMBER_OF_SAMPLES=50

DATASET=mmlu

bash eval.sh $DATASET $MODEL_NAME $BATCH_SIZE $EVAL_MODE $OVERWRITE $NUMBER_OF_SAMPLES $GPU 

# Results:
# The results would be like:
# {
#     "accuracy": 0.507615302109403,
#     "category_acc": {
#         "high_school_european_history": 0.6585365853658537,
#         "business_ethics": 0.6161616161616161,
#         "clinical_knowledge": 0.5,
#         "medical_genetics": 0.5555555555555556,
#    ...

A full list of dataset names can be found here.

📚 Supported Models and Datasets

Datasets

Dataset	Metrics	Status
cross_xquad	AC3, Consistency, Accuracy	✅
cross_mmlu	AC3, Consistency, Accuracy	✅
cross_logiqa	AC3, Consistency, Accuracy	✅
sg_eval	Accuracy	✅
cn_eval	Accuracy	✅
us_eval	Accuracy	✅
ph_eval	Accuracy	✅
flores_ind2eng	BLEU	✅
flores_vie2eng	BLEU	✅
flores_zho2eng	BLEU	✅
flores_zsm2eng	BLEU	✅
mmlu	Accuracy	✅
c_eval	Accuracy	✅
cmmlu	Accuracy	✅
zbench	Accuracy	✅
indommlu	Accuracy	✅
ind_emotion	Accuracy	✅
ocnli	Accuracy	✅
c3	Accuracy	✅
dream	Accuracy	✅
samsum	ROUGE	✅
dialogsum	ROUGE	✅
sst2	Accuracy	✅
cola	Accuracy	✅
qqp	Accuracy	✅
mnli	Accuracy	✅
qnli	Accuracy	✅
wnli	Accuracy	✅
rte	Accuracy	✅
mrpc	Accuracy	✅
sg_eval_v1_cleaned	Accuracy	✅

Models

Model	Size	Mode	Status
Meta-Llama-3-8B-Instruct	8B	0-Shot	✅
Meta-Llama-3-70B-Instruct	70B	0-Shot	✅
Meta-Llama-3.1-8B-Instruct	8B	0-Shot	✅
Qwen2-7B-Instruct	7B	0-Shot	✅
Qwen2-72B-Instruct	72B	0-Shot	✅
Meta-Llama-3-8B	8B	5-Shot	✅
Meta-Llama-3-70B	70B	5-Shot	✅
Meta-Llama-3.1-8B	8B	5-Shot	✅

How to evaluate your own model?

To use SeaEval to evaluate your own model, you can just add your model to model.py and model_src accordingly.

📚 Citation

If you find our work useful, please consider citing our paper!

SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning

@article{SeaEval,
  title={SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning},
  author={Wang, Bin and Liu, Zhengyuan and Huang, Xin and Jiao, Fangkai and Ding, Yang and Aw, Ai Ti and Chen, Nancy F.},
  journal={NAACL},
  year={2024}
}

CRAFT: Extracting and Tuning Cultural Instructions from the Wild

@article{wang2024craft,
  title={CRAFT: Extracting and Tuning Cultural Instructions from the Wild},
  author={Wang, Bin and Lin, Geyu and Liu, Zhengyuan and Wei, Chengwei and Chen, Nancy F},
  journal={ACL 2024 - C3NLP Workshop},
  year={2024}
}

CrossIn: An Efficient Instruction Tuning Approach for Cross-Lingual Knowledge Alignment

@article{lin2024crossin,
  title={CrossIn: An Efficient Instruction Tuning Approach for Cross-Lingual Knowledge Alignment},
  author={Lin, Geyu and Wang, Bin and Liu, Zhengyuan and Chen, Nancy F},
  journal={arXiv preprint arXiv:2404.11932},
  year={2024}
}

Contact: [email protected]