LangBridge

Repository for the paper "LangBridge: Multilingual Reasoning Without Multilingual Supervision".

Links

• Paper
• Tweet (brief overview of the paper)

TL;DR

🤔LMs good at reasoning are mostly English-centric (MetaMath, Orca 2, etc).

😃Let’s adapt them to solve multilingual tasks. BUT without using multilingual data!

LangBridge “bridges” mT5 encoder and the target LM together while utilizing only English data. In test time, LangBridge models can solve multilingual reasoning tasks effectively.

🤗Models

Llama 2

• llama2-langbridge-9b

Llemma

• llemma-langbridge-9b

MetaMath

• metamath-langbridge-9b
• metamath-langbridge-15b
• metamath-langbridge-20b

Code Llama

• codellama-langbridge-9b
• codellama-langbridge-15b
• codellama-langbridge-20b

Orca 2

• orca2-langbridge-9b
• orca2-langbridge-15b
• orca2-langbridge-20b

Install

Using the Models only

pip install -e .

Replicating the evaluation from the paper

pip install -e .
pip install -e bigcode-evaluation-harness
pip install -e evaluation-harness

Usage

Quick usage example

from transformers import AutoTokenizer
from langbridge import LangBridgeModel

# our pretrained langbridge models all leverage this encoder tokenizer
enc_tokenizer = AutoTokenizer.from_pretrained('kaist-ai/langbridge_encoder_tokenizer') 
lm_tokenizer = AutoTokenizer.from_pretrained('kaist-ai/metamath-langbridge-9b')
model = LangBridgeModel.from_pretrained('kaist-ai/metamath-langbridge-9b').to('cuda')


metamath_template = (
    "Below is an instruction that describes a task. "
    "Write a response that appropriately completes the request.\n\n"
    "### Instruction:\n{instruction}\n\n### Response:\n"
    )
question = "문제: Jimmy는 Ethel이 가진 돈의 두배보다 2달러가 더 많습니다. Ethel이 8달러가 있다고하면, Jimmy는 얼마를 갖고 있나요?  정답: "
prefix =  metamath_template.format(instruction=question)
output = model.generate_from_prefix(enc_tokenizer, lm_tokenizer, prefix=prefix)
print(output)

If Ethel has 8 dollars, then Jimmy has 2 * 8 + 2 = 18 dollars.
Therefore, Jimmy has 18 dollars.
#### 18
The answer is: 18

Tips

1. Set the prefixes as if you were prompting the original LMs. For example, for Orca 2-langbridge use the Orca 2 template. For pretrained models (Llama 2, Llemma, and Code Llama), you may need to use few-shot examples.
2. The encoder tokenizer is simply an mT5 tokenizer with whitespace tokens. The reason for the added whitespaces is explained in section D.1 of the paper.

Training Example

cd python_scripts
bash scripts/train_lb/metamath.sh

Tips

1. For optimal performance, keep freeze_encoder=False for pretrained LMs (trained on unlabeled corpora), and freeze_encoder=True for finetuned LMs (trained on labeled corpora). This is explained in section D.1 of the paper.
2. The training and validation data should have two columns: input and output. The output should be empty for unlabeled corpora. In this case pass output_exists=False, then the code will dynamically create the label(output) by splitting the input. The output shouldn't be empty for labeled corpora. In this case pass output_exists=True.
3. When training on output_exists=False, set use_dynamic_enc_length=True. See section 4.1. use_dynamic_enc_length flag won't have an effect when output_exists=True.

Evaluation Example

cd python_scripts
bash scripts/eval/mgsm/metamath-lb-9b.sh

Limitation

LangBridge mostly helps for low-resource languages. If the language model is already proficient in a certain language, LangBridge may lower performance in that language. Please refer to the paper for the detailed evaluation results.