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paper-PTODSKC

This repository contains code and access to the dataset used for our paper titled Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large Language Models. This document is intended for researchers, developers and those who would like to build, run, and experiment with paper-PTODSKC.

Prerequisites and Dependencies

• requires M series Apple silicon
• requires macOS >= 13.5
• requires native Python >= 3.8. It's recommended to use a virtual environment to manage dependencies. In our work, we created virtual environment directory named venv under the repository root folder and used it run Python from code as venv/bin/python3. It's important to note that if you opt to use a different version of Python, you will need to update the Python path in runBenchmarks.py and fineTunedShot.py.

Installation

mlx-lm is available on PyPI. Please refer to the official MLX documentation and MLX examples for more details on the MLX platform.
To install the Python API with all the requirements, run:

python3 -m pip install -r requirements.txt

How To Use

Fine-tuning datasets

Datasets needed to generate the KC QLoRA adapter exists under the data directory. base.jsonl file under this directory is the fundamental dataset used for the training and testing.

data/train.jsonl, data/test.jsonl, and data/valid.jsonl files holds the user-prompts and prompt response pairs for train, test and validation datasets for the entire fine-tuning set, respectively.data/2/, data/4/, and data/8/ folders holds the same structure for 2, 4, and 8 prompts per ontology concepts that are used in the performance evaluation section of the paper.

• Please note that test and validation sets holds the same prompts to be able to ease the control the quality of the QLoRA adapter generation process.

Generating ground-truth file

generateGroundTruth.py script processes the data/test.jsonl file line-by-line and writes the expected prompt response for each user input to a separate file under results/test_ground_truth_output/. The output of each line is written to a different file in the followign format, first line's output is written to 1.ttl file etc... The generated ground-truth file will be used in performance evaluations.

To generate the results/test_ground_truth.jsonlfile, run the following command:

python3 scripts/generateGroundTruth.py 

Model file

In our work, we utilize the 4-bit quantized and mlx-converted version of the Mistral-7B-Instruct-v0.2 model. All model files must be placed under the model/Mistral-7B-Instruct-v0.2-4bit-mlx folder located in the main directory of our repository. To replicate our test results accurately, please download the mlx-community/Mistral-7B-Instruct-v0.2-4bit-mlx file from the mlx-community on Hugging Face and ensure it is placed in the specified path.

Adapters

The generated QLoRA adapters are saved under the adapters/ directory.

Due to backward-incompatible changes made in the MLX framework, the current MLX versions cannot be run with the old adapters. The adapters that produced the results obtained in our study are saved under the adapters\ directory for historical reasons.

Fine-tuning

QLoRA adapter creation can be done with the following command. Please feel free to update the QLoRA training parameters:

python3 -m mlx_lm.lora --train --model mlx-community/Mistral-7B-Instruct-v0.2-4bit-mlx --iters 600 --data ./data --batch-size 4 --lora-layers 16 --adapter-path adapters/adapters_b4_l16_i612_ts8sample_mistral_I_v02_4b.npz

Running the benchmarks

To be able to evaluate the generated adapter with the model please use the runBenchmarks.py script with --model-path, --adapter-path, and --testset-path arguments. This script calls fineTunedShot.py, reading input from testset-path and writing the results to the results/test_evaluation_output/ directory.

python3 scripts/runBenchmarks.py --model-path mlx-community/Mistral-7B-Instruct-v0.2-4bit-mlx --adapter-path adapters/adapters_b4_l16_i612_ts8sample_mistral_I_v02_4b.npz --testset-path data/test.jsonl

Evaluation

calculateF1Score.py script compares each method's result file with the ground-truth file and calculates precision, recall and f1-score. All results are written to the evaluation_results.txt file under results directory.

python3 scripts/calculateF1Score.py

Cite

@misc{coplu2024ontologydriven,
      title={Prompt-{Time} {Ontology}-{Driven} {Symbolic} {Knowledge} {Capture} with {Large} {Language} {Models}},
      author={Tolga Çöplü and Arto Bendiken and Andrii Skomorokhov and Eduard Bateiko and Stephen Cobb},
      year={2024},
      eprint={2405.14012},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}