metadata

license: other
library_name: peft
tags:
  - llama-factory
  - lora
  - generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
model-index:
  - name: sft_trained_woaqa_llama3
    results: []
datasets:
  - jiazhengli/Synthetic_Rationale
  - jiazhengli/Rationale_MCTS
language:
  - en
metrics:
  - accuracy
  - f1

Meta-Llama-3-8B-QLoRA-Assessment-Rationale-sft

The model trained with w/o private data from the EMNLP 2024 Paper: Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring.

Paper: Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring (EMNLP 2024 Findings)
GitHub Repository: Thought Tree Assessment Repository

Intended uses & limitations

This model offers a valuable resource for research in explainable AI within educational technology. The model is trained with noisy response-level rationales. This makes them unsuitable for direct application in high-stakes assessments without additional verification.

Training and evaluation data

We trained and evaluated the model on the Synthetic Rationale data, which was generated from the Rationale MCTS data.

To extract scores from rationales, please use the jiazhengli/deberta-v3-large-Rationale-to-Score.

Citation

Please cite the following work if you utilize this model:

BibTeX:

@misc{li2024calibratingllmspreferenceoptimization,
      title={Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring}, 
      author={Jiazheng Li and Hainiu Xu and Zhaoyue Sun and Yuxiang Zhou and David West and Cesare Aloisi and Yulan He},
      year={2024},
      eprint={2406.19949},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.19949}, 
}

Training procedure

Please refer to our paper.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 4.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.9813	0.63	100	0.9671
0.9108	1.26	200	0.9250
0.8976	1.9	300	0.9091
0.8687	2.53	400	0.9005
0.8548	3.16	500	0.8958
0.8468	3.79	600	0.8952

Framework versions

PEFT 0.10.0
Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2