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Enhancing Event Reasoning in Large Language Models through Instruction Fine-Tuning with Semantic Causal Graphs

Create Environment

conda create --name scg-event \
    python=3.10 \
    pytorch-cuda=12.1 \
    pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers \
    -y
conda activate scg-event
pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps "trl<0.9.0" peft accelerate bitsandbytes
pip install einops
pip install anthropic openai

Usage

/baseline_responses contains scripts used to obtain baseline responses of open-source and proprietary models on the event detection task.

/data contains the original event detection datasets and scripts for creating SCG instructions for event detection.

/event_instruct contains training scripts for training LLMs on the datasets in /data using LoRA and inference scripts for gathering responses of trained models.

/event_dpo contains training and inference scripts for the Direct Preference Optimization experiments.

/eval_event contains evaluation scripts of the parsed responses from trained LLMs on the test sets of the event detection datasets.

/change_context contains the script to make calls to Anthropic API to create modified context variations of the event detection test sets. Folder also contains the modified test sets we created that were used in our experiments in /change_context/modified_data.

/eval_llm contains scripts for performing general LLM benchmark testing by leveraging the Language Model Evaluation Harness

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