Large Language Models Are Reasoning Teachers

Official repository for Large Language Models Are Reasoning Teachers, by Namgyu Ho, Laura Schmid, and Se-young Yun.

🚀 Accepted to ACL 2023.

This repository contains code for (1) running CoT reasoning on OpenAI models, and (2) apply Fine-tune-CoT to train students based on OpenAI models or custom models such as T5, GPT-2 on your GPUs.

Getting Started

OpenAI API Experiments

OpenAI API experiments are implemented in the oai module. Refer to notebooks/example_oai_finetune_cot.ipynb on how to run Fine-tune-CoT from start to finish.

Custom Experiments (on GPU)

Custom experiments are implemented in the custom module, based on PyTorch Lightning. Refer to main.py and scripts/custom/*.sh on how to fine-tune models such as T5, Flan-T5, and GPT-2 using Fine-tune-CoT.

Setup

pip install -r requirements.txt
python setup.py develop

Resources

Teacher, student inference data

Will be available for download soon!

Template-based split (paper Appendix E.3)

Template-based splits for MultiArith and Date Understanding are saved in /data/splits/*__template.json

Few-shot Prompts

Few-shot prompts adapted from Wei 2022 are saved in /data/few_shot_cot_prompts.json

Data Structures

`data.dataset.Dataset`

{
  "metadata": {
    "dataset_key": "multiarith"
  },
  "data": [
    {
      "sample_index": 0,
      "question": "string",
      "answer": "string",
      "rationale": "string?"
    }
  ]
}

`data.completion.CompletionDataset`

{
  "metadata": {
    "dataset_key": "multiarith",
    "base_model": "curie",
    "finetune_key": "zs_cot_multiarith",
    "train_key": "ft_cot",
    "prediction_template": "ft_cot_token",
  },
  "data": {
    "<sample_index>": [
      {
        "sample_index": 0,
        "completion_index": 0,
        "question": "string",
        "answer": "string",
        "prompt": "string",
        "completion": "string",
        "finish_reason": "string",
        "reasoning_prompt": "string?",
        "reasoning_completion": "string?",
        "reasoning_finish_reason": "string?",
      }
    ]
  }
}

Data Organization

Needs update.

<model_key> = B_<base_model>_T_<train_key>

File Organization Pattern

saved/
|–– completion_data/
    |–– B_<BASE_MODEL>__C_<COMPLETION_KEY>/
        |-- D_<DATESET_KEY>.json  # base model inference
        |-- F_<FINETUNE_KEY>__D_<DATESET_KEY>.json  # default fine-tuned model inference
        |-- F_<FINETUNE_KEY>__T_<TRAIN_KEY>__D_<DATESET_KEY>.json  # custom fine-tuned model inference
|–– finetune_data/
    |–– P_<PLATFORM_KEY>/
        |–– F_<FINETUNE_KEY>{.*|/}
|–– model_metadata/
    |–– B_<base_model>
        |–– F_<FINETUNE_KEY>__T_<train_key>.json

File Organization Examples

saved/
|–– completion_data/
    |–– B_text-davinci-002__C_zs_cot/
    |–– B_text-davinci-002__C_zs_cot_long/
    |–– B_text-davinci-002__C_fs_cot/
    |–– B_curie__C_zs_cot/
    |–– B_curie__C_fs_cot/
    |–– B_curie__C_zs/
    |–– B_curie__C_ft_cot/
|–– finetune_data/
    |–– F_zs_cot_multiarith/  # text-davinci-002_zs_cot
    |–– F_zs_cot_long_multiarith/
|–– model_metadata/
    |–– B_curie/
        |–– F_zs_cot_multiarith.json

ckqqqq / reasoning-teacher Goto Github PK

reasoning-teacher's Introduction

Large Language Models Are Reasoning Teachers

Getting Started

OpenAI API Experiments

Custom Experiments (on GPU)

Setup

Resources

Teacher, student inference data

Template-based split (paper Appendix E.3)

Few-shot Prompts

Data Structures

data.dataset.Dataset

data.completion.CompletionDataset

Data Organization

File Organization Pattern

File Organization Examples

Personal Note

reasoning-teacher's People

Contributors

Recommend Projects

Recommend Topics

Recommend Org

`data.dataset.Dataset`

`data.completion.CompletionDataset`