Extrapolating Large Language Models to Non-English by Aligning Languages
This repository contains the code implementation for the project that aims to empower pre-trained Large Language Models (LLMs) on non-English languages by building semantic alignment across languages. The project explores cross-lingual instruction-tuning and multilingual instruction-tuning techniques. The code implementation is based on Stanford Alpaca.
Requirements and Installation
To install this repository, follow these steps:
git clone [email protected]:NJUNLP/x-LLM.git
cd x-LLM
pip install --editable ./
For detailed information about the conda environment, refer to the environment.yml file.
Usage
Download Pre-trained LLM
Start by downloading the pre-trained LLM into the ./model directory.
Download Dataset
You can download all the datasets used in this project from this link. Once downloaded, place the datasets in the ./data directory. The datasets include:
- Training dataset
- Alpaca
- Wikimatrix
- Newscommentary
- Evaluation dataset
- XQUAD
- MLQA
- Flores-101
- MI-Eval
Load Raw Data Along with Instruction
You can load raw data along with instruction using the provided scripts (./data//<dataset.py>). If you want to use a new dataset, you need to implement the corresponding script. The loaded data will have the following structure:
datasets.Features(
{
"id": datasets.Value("string"),
"instruction": datasets.Value("string"),
"input": datasets.Value("string"),
"output": datasets.Value("string")
}
)
Instruction-tune Pre-trained LLM
To instruction-tune the pre-trained LLM, run the train.sh script. For example, you can instruction-tune LLaMA-7B to x-LLaMA-7B (Chinese) with the following command:
bash script/train.sh llama-7b-hf alpaca_en+alpaca_zh+translation_ncwm_en-zh
In this command, the first argument denotes the pre-trained LLM to use, and the second argument represents the training data to use. You can use + to concatenate multiple datasets, and the training data will be shuffled by the Huggingface Trainer.
Once the training is complete, the finetuned LLM will be saved in ./model/llama-7b-hf.alpaca_en+alpaca_zh+translation_ncwm_en-zh.finetune. You can use aliases to define shorter names, and more details can be found in ./data/alias/alias.json.
Test Finetuned LLM
To test the finetuned LLM, run the inference.sh script. For example, you can test the tuned LLM on the Flores dataset with the following command:
bash script/inference.sh llama-7b-hf.alpaca_en+alpaca_zh+translation_ncwm_en-zh.finetune translation_flores_en-zh
The output results will be saved in model/llama-7b-hf.alpaca_en+alpaca_zh+translation_ncwm_en-zh.finetune/test/translation_flores_en-zh.inference.jsonl. The prediction field represents the generated content of the LLM.
Interact with LLM Through Web UI
To interact with the LLM through a web UI, run app.py with the following command:
bash app.py model/llama-7b-hf.alpaca_en+alpaca_zh+translation_ncwm_en-zh.finetune
Citation
If you find this repository helpful, please consider citing our paper:
@misc{zhu2023extrapolating,
title={Extrapolating Large Language Models to Non-English by Aligning Languages},
author={Wenhao Zhu and Yunzhe Lv and Qingxiu Dong and Fei Yuan and Jingjing Xu and Shujian Huang and Lingpeng Kong and Jiajun Chen and Lei Li},
year={2023},
eprint={2308.04948},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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