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Examining the Power of Symbolic Tasks in Instruction Tuning

This is the official repository for the paper "From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning". In this paper, we introduce a straightforward yet effective method for enhancing instruction tuning by employing symbolic tasks.

🔥 Updates

🏴󠁶󠁵󠁭󠁡󠁰󠁿 Overview

Fine-tuning language models on tasks with instructions has demonstrated potential in facilitating zero-shot generalization to unseen tasks. In this paper, we introduce a straightforward yet effective method for enhancing instruction tuning by employing symbolic tasks. Compared to crowd-sourced human tasks or model-generated tasks, symbolic tasks present a unique advantage as they can be easily generated in vast quantities, theoretically providing an infinite supply of high-quality training instances.

📚 Dataset

We host the dataset on HuggingFace Datasets. You can load the dataset by running:

from datasets import load_dataset
dataset = load_dataset("sail/symbolic-instruction-tuning")

Note the dataset is a dict with three keys: train, validation and test. Each key contains the following files:

├── train
│   ├── flan.json # the 600K subset of the FLAN v2 dataset
│   ├── sql.json # the 200K symbolic data of the SQL execution
├── validation
│   ├── wtq_mmlu.json # the mixture of WTQ and MMLU validation sets for validation
├── test
│   ├── [task_name]_tapex_[scale].json # the test file with the prompt selected on the validation set of the corresponding task, for the specific scale

We also host the model weights on HuggingFace Hub. You can load the model by running:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("sail/tapex-zero-xl")
model = AutoModelForSeq2SeqLM.from_pretrained("sail/tapex-zero-xl")

⚡️ Usage

Requirements

The key requirements are as below:

  • Python 3.6+
  • PyTorch 1.12+
  • Transformers 4.24.0+

You can also install the requirements by running:

pip install -r requirements.txt

Run

Multi-Task Instruction Tuning

To run tapex-zero-large, use the following command while keeping in mind that the configuration is designed for a single GPU card with 40GB of memory. If needed, you can adjust the number of GPU cards in train_config.yaml and the batch size using the arguments to match your specific computing environment.

accelerate launch --config_file train_config.yaml train_model.py \
  --model_name_or_path google/flan-t5-large \
  --dataset_name sail/symbolic-instruction-tuning \
  --eval_func get_denotation_accuracy \
  --input_column input \
  --output_column output \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 2 \
  --per_device_eval_batch_size 6 \
  --gradient_accumulation_steps 32 \
  --learning_rate 3e-5 \
  --preprocessing_num_workers 16 \
  --generation_max_length 128 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --max_steps 20000 \
  --logging_strategy steps \
  --logging_steps 10 \
  --evaluation_strategy steps \
  --predict_with_generate \
  --warmup_steps 1000 \
  --max_seq_length 2048 \
  --max_answer_length 128 \
  --val_max_answer_length 128 \
  --output_dir checkpoints/tapex_zero_large \
  --run_name tapex_zero_large

To run tapex-zero-xl, use the following command, keeping in mind that the configuration is intended for a single GPU card with 40GB of memory. Please note that the --bf16 flag is only compatible with A100 hardware and has not been tested on other hardware configurations.

deepspeed train_model.py \
  --model_name_or_path google/flan-t5-xl \
  --dataset_name sail/symbolic-instruction-tuning \
  --eval_func get_denotation_accuracy \
  --input_column input \
  --output_column output \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 1 \
  --per_device_eval_batch_size 2 \
  --gradient_accumulation_steps 64 \
  --learning_rate 3e-5 \
  --preprocessing_num_workers 16 \
  --generation_max_length 128 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --max_steps 20000 \
  --logging_strategy steps \
  --logging_steps 10 \
  --evaluation_strategy steps \
  --predict_with_generate \
  --warmup_steps 1000 \
  --max_seq_length 2048 \
  --max_answer_length 128 \
  --val_max_answer_length 128 \
  --bf16 \
  --output_dir checkpoints/tapex_zero_xl \
  --run_name tapex_zero_xl  \
  --deepspeed deepspeed_config/zero_stage1_config.json

💬 Citation

If you find our work is helpful, please cite as:

@article{liu2023zero,
  title={From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning},
  author={Liu, Qian and Zhou, Fan and Jiang, Zhengbao and Dou, Longxu and Lin, Min},
  eprint={2304.07995},
  year={2023}
}

👍 Contributing

We welcome contributions and suggestions!