This repo is for our EMNLP2023 Findings paper: InstOptima: Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction Operators.

Instruction-based language modeling has received significant attention in pretrained language models. However, the efficiency of instruction engineering remains low and hinders the development of instruction studies. Recent studies have focused on automating instruction generation, but they primarily aim to improve performance without considering other crucial objectives that impact instruction quality, such as instruction length and perplexity. Therefore, we propose a novel approach (i.e., InstOptima) that treats instruction generation as an evolutionary multi-objective optimization problem. In contrast to text edition-based methods, our approach utilizes a large language model (LLM) to simulate instruction operators, including mutation and crossover. Furthermore, we introduce an objective-guided mechanism for these operators, allowing the LLM to comprehend the objectives and enhance the quality of the generated instructions. Experimental results demonstrate improved fine-tuning performance and the generation of a diverse set of high-quality instructions.

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
chmod +x ./Miniconda3-latest-Linux-x86_64.sh
./Miniconda3-latest-Linux-x86_64.sh

conda create -n instoptima python=3.9
conda activate instoptima

conda install pytorch pytorch-cuda -c pytorch -c nvidia

pip install -r requirements.txt

For all tasks, we can start the chatbot by running the following command:

python chatbot.py

This will start the chatbot service on port 6789. You can then send a POST request to the chatbot service to get the response. By using this chatbot, all the instructions will be generated by the LLM-based instruction operators and archived in the current directory.

For the ABSA task, please revise the config in the main.py file and run the following command:

python main.py

This is the initial version of our code. We will update the code and add more examples in the future. If you have any questions, please feel free to contact us.

@inproceedings{YangL23instoptima,
    title = "{I}nst{O}ptima: Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction Operators",
    author = "Yang, Heng  and
      Li, Ke",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.907",
    pages = "13593--13602",
    abstract = "Instruction-based language modeling has received significant attention in pretrained language models. However, the efficiency of instruction engineering remains low and hinders the development of instruction studies. Recent studies have focused on automating instruction generation, but they primarily aim to improve performance without considering other crucial objectives that impact instruction quality, such as instruction length and perplexity. Therefore, we propose a novel approach (i.e., InstOptima) that treats instruction generation as an evolutionary multi-objective optimization problem. In contrast to text edition-based methods, our approach utilizes a large language model (LLM) to simulate instruction operators, including mutation and crossover. Furthermore, we introduce an objective-guided mechanism for these operators, allowing the LLM to comprehend the objectives and enhance the quality of the generated instructions. Experimental results demonstrate improved fine-tuning performance and the generation of a diverse set of high-quality instructions.",
}