2251821381/cot_distillation_acl2024

Learning to Maximize Mutual Information for Chain-of-Thought Distillation

Official PyTorch implementation of ACL2024 paper Learning to Maximize Mutual Information for Chain-of-Thought Distillation
TL;DR: This paper formulates Chain-of-Thought (CoT) distillation with information bottleneck and introduces a variational method to estimate mutual information to improve CoT distillation performance.\ The code is based on repo of Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Environment Setup

• Setup Conda environment:

conda create --name distill python=3.10.6 -y
conda activate distill
conda install -y pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
pip install git+https://github.com/huggingface/[email protected] datasets sentencepiece protobuf==3.20.* tensorboardX

• Extract datasets to datasets/:

unzip datasets.zip

Training Command Usages

Args usages

• --from_pretrained: google/t5-v1_1-small, google/t5-v1_1-base, google/t5-v1_1-large, google/t5-v1_1-xxl
• --dataset: esnli, anli1, cqa, svamp
• --label_type:
  • --label_type gt: Use GT label for training
  • --label_type llm: Use LLM predicted label for training
• --alpha 0.5: recommended
• --beta 0.1: recommended
• --batch_size: Batch size
• --grad_steps: Gradient accumulation step
• --max_input_length: Maximum input length
• --eval_steps: How many steps to evaluate the model during training
• --max_steps: Maximum steps for training
• --run: Random seed to use
• --model_type:
  • standard: Standard finetuning (--label_type gt) or distillation (--label_type llm)
  • task_prefix: Distilling step-by-step
• --parallelize: Model parallelism
• --CoT_Distill:
  • --CoT_Distill True: Use CoT distillation, recommended
  • --CoT_Distill False: No CoT distillation

Example usages

• Standard finetuning:

python main.py --from_pretrained google/t5-v1_1-base --dataset cqa --model_type standard --label_type gt --batch_size 64

• Distilling step-by-step with GT label and PaLM rationale:

python main.py --from_pretrained google/t5-v1_1-base --dataset cqa --model_type task_prefix --label_type gt --llm palm --alpha 0.5 --batch_size 64

• Standard distillation:

python run.py --from_pretrained google/t5-v1_1-base --dataset cqa --model_type standard --label_type llm --batch_size 64

• Distilling step-by-step with PaLM label and PaLM rationale:

python run.py --from_pretrained google/t5-v1_1-base --dataset cqa --model_type task_prefix --label_type llm --llm palm --alpha 0.5 --batch_size 64

T5 Confidence and Calibration Analysis

The script requires specific command-line arguments to run. The primary arguments include specifying the dataset, the training set, and the path to the model checkpoint.

python uncertainty.py --dataset [dataset_name] --trainset [trainset_name] --ckpt_path [path_to_checkpoint]

• --dataset: Specify the dataset name. Supported datasets are 'cqa', 'svamp', 'esnli', and 'anli1'.
• --trainset: (Optional) Specify the training dataset name (what training set has the model been trained on). Default is "vanilla".
• --ckpt_path: (Optional) Provide the path to the model checkpoint. If not provided, the script defaults to using "T5 Small".

Output will be saved under "dataset_{dataset}_model{trainset}_calib_results.txt".

GPU Support

The script detects and utilizes a GPU if available, ensuring efficient processing.

Script to run T5 to generate and evaluate CoT

Evaluation Metrics: BLEU, ROBERTA-based Cosine Similarity (could continuously add more)

Pre-requisite:

pip install evaluate 

Usage:

python explain_test.py --dataset DATASET --ckpt_path CHECKPOINT_PATH 

Example:

python explain_test.py --dataset esnli --ckpt_path esnli_data/esnli_cpt --input ./datasets/cqa/llm/test_CoT_0.json

This would run on esnli test set with esnli checkpoint. The output is a file named as dataset_esnli_model_esnli_data_esnli_cpt_explain.txt, where each line is a pair of Gold COT and predicted COT.

Example:

GOLD COT: The church choir singing to the masses does not necessarily mean that the church has cracks in the ceiling. || PRED COT: The church choir is singing to the masses, not cracks in the ceiling.

Cite

If you find this repository useful, please consider citing:

@inproceedings{chen2024learning,
  title={Learning to Maximize Mutual Information for Chain-of-Thought Distillation},
  author={Chen, Xin and Huang, Hanxian and Gao, Yanjun and Wang, Yi and Zhao, Jishen and Ding, Ke},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2024},
  year={2024}
}