Large Language Models are Parallel Multilingual Learners

The code of the paper-Large Language Models are Parallel Multilingual Learners includes two parts. The first one is counting activated neurons in the multi-layer perceptrons (MLPs) of transformer models. The second one is fine-tuning and inference.

1 Requirements and Installation

• LLaMA-Factory >= 0.3.2

To do fine-tuning and inference on the multilingual LLMs used in our work, you may need to create an environment satisfying the requirements of LLaMA-Factory.

2 Parallel Multilingual Data

The parallel multilingual data translated by GPTs is released in gpt_translated_data/specific dataset. The datasets used in our experiments are detailed in the following table. Note that all the samples are randomly selected to guarantee the effectiveness of the evaluations. Except for the FLORES-200 and XNLI development set which is already parallel in multiple languages, other datasets are translated by GPTs.

Task	Evaluation Dataset	Training Set	Test Set	Translation System
Translation	WMT	FLORES-200 development set	WMT22 (de2en, zh2en, de2fr, en2de, en2zh) and WMT21 (is2en)	GPT4
Nature Language Inference	RTE	same data as below	RTE devlopment set	ChatGPT
Nature Language Inference	XNLI	XNLI development set	1000 samples of the XNLI test set for each language (fr, de, ru, zh)	ChatGPT
Reading Comprehension	BoolQ	1000 samples of the BoolQ training set	1000 samples of the BoolQ development set	ChatGPT
Text Simplification	Asset	2000 samples of the Asset development set	Asset test set	ChatGPT
Text Simplification	Wiki-Auto	same data as above	1000 samples of the Wiki-Auto development set	ChatGPT
Abstractive Summarization	XLSum	300 samples of the XLSum development set for each language	500 samples of the XLSum test set for each language (fr, ru, es)	ChatGPT

3 Statistics of Activated Neurons

To determine the number of activated neurons, we need to analyze the intermediate results generated by MLPs during inference. This process involves modifying the modeling files to enable MLPs to record intermediate status (Section 3.1), running inference and writing the status into files (Section 3.2), and finally reading the result files for analysis (Sections 3.3-3.5). It's worth noting that during this process, the Qwen models we used are finetuned (Section 4), while the Bloomz models are not. Since the MLPs of Qwen are in the same architecture as those of LLaMA-2, you can duplicate the modification of modeling_qwen.py to modeling_llama.py and count LLaMA's activated neurons.

3.1 Modification of modeling Files

To obtain intermediate result files, we replace the original modeling files with those we have modified. The modified modeling files we used for Bloomz and Qwen models are provided under ./counting_activated_neurons/modeling/. The script below is an example of a replacement:

# Replace Bloomz modeling file
mv transformers/src/transformers/models/bloom/modeling_bloom.py transformers/src/transformers/models/bloom/modeling_bloom_ori.py     # Back up the original modeling file
cp ./counting_activated_neurons/modeling/modeling_bloom.py transformers/src/transformers/models/bloom/

3.2 Inference

Run the script ./counting_activated_neurons/activate/infer_activate.py to do inference. Three output files can be obtained from this process: the model generation result file, the activated neuron proportion of each layer file, and the activated neuron status of each position file.

Description of parameters in the script:

os.environ['CUDA_VISIBLE_DEVICES']=""     # specify GPUs to be used
checkpoint = ""     # path of model
file_name = ""     # name of input file（not include file suffix）
input_file = f"{file_name}.txt"     # path of input file
activate_proportion_file = f"{file_name}-proportion-{checkpoint}.jsonl"     # path of the activated neurons proportion of each layer file
activate_index_file = f"{file_name}-index-{checkpoint}.jsonl"     # path of the activated neurons status of each position file
output_file = f"{file_name}-result-{checkpoint}.txt"     # path of the model generation result file
sys_prompt_flag = False     # Additional prompt settings, True to use sys_prompt, False to not use additional prompts
sys_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|custom|><|im_end|>\n<|im_start|>assistant\n"     # the <| custom |> will be replaced with input information
interactivate_flag = False     # interactive mode settings, True to use interactive mode, False to use read file mode

Run:

python ./counting_activated_neurons/activate/infer_activate.py

3.3 Analysis of Activated Neurons Proportion of Each Layer

Run the ./counting_activated_neurons/activate/Proportion_analysis.py script to analyze the activated neurons proportion of each layer file and obtain the final results. The results include the proportion of inhibited neurons in each layer and the total average inhibition proportion.

Description of script parameters:

input_file = ''     # the activated neurons proportion of each layer file generated during the inference process

Run:

python ./counting_activated_neurons/activate/Proportion_analysis.py

3.4 Visualization of All Activated Neurons

Run the ./counting_activated_neurons/HeatMap_analysis.py script to visualize the activated neurons' distribution with a heatmap.

Description of script parameters:

file = ""     # the activated neurons status of each position file generated during the inference process

Run:

python ./counting_activated_neurons/activate/HeatMap_analysis.py

Results:

3.5 Analysis of High-frequency Activated Neurons

Run the ./counting_activated_neurons/Peak_analysis.py script to make line graphs of the number and the distribution of high-frequency activated neurons in each layer.

Description of script parameters:

file_list = []     # input file list, the number of lists can be 1
select_proportion = 0.2     # Select the proportion of neurons, for example, if set to 0.2, it means selecting the top 20% of neurons with activation count

Run:

python ./counting_activated_neurons/activate/Peak_analysis.py

Results:

4 Fine-tuning and Inference

We use LLaMA-Factory and their official code to do fine-tuning and inference, which is detailed in the table below. The setup of fine-tuning and inference is provided in the appendix of our paper.

Model	LoRA tuning or Not	Tool of Fine-tuning and Inference
ChatGPT	N	OpenAI's API
Qwen-7B	Y	LLaMA-Factory
Qwen-14B	Y	LLaMA-Factory
Qwen-72B	Y	LLaMA-Factory
ALMA-13B	Y	LLaMA-Factory
mT0-13B	N	offical code
Yi-34B	Y	LLaMA-Factory
Bloomz-176B	N	offical code

The code we used for making fine-tuning and inference data is provided in fine-tuning_and_inference.

Citation

If this work is helpful for your research, please consider citing the following BibTeX entry.

@article{mu2024large,
  title={Large Language Models are Parallel Multilingual Learners},
  author={Mu, Yongyu and Feng, Peinan and Cao, Zhiquan and Wu, Yuzhang and Li, Bei and Wang, Chenglong and Xiao, Tong and Song, Kai and Liu, Tongran and Zhang, Chunliang and others},
  journal={arXiv preprint arXiv:2403.09073},
  year={2024}
}