README.md

UaIT

Can LLMs Learn Uncertainty on Their Own? Expressing Uncertainty Effectively in A Self-Training Manner (EMNLP 2024)

NOTE: This is the initial version code and will be updated and improved recently.

Environments

Please config environment by following requirements.txt.

Data Preparing

Training Dataset: https://drive.google.com/file/d/13z_qrVOBlgu75IJBpX-1vMSCC6hC9yH4/view?usp=sharing Download and set the path in parse_triviaqa_ft_chat.py

cd src/parse_datasets
python parse_triviaqa_ft_chat.py

Uncertainty Estimation

for the Trivia QA dataset:

cd src
sh scripts/trivia_qa/ue_pipeline_llama2-chat-7b.sh

FT Data Construction

cd src/finetune
python get_ft_data.py --train-data-path [train-data-path] --train-data-auroc [train-data-auroc]

Uncertainty-aware Instruction Tuning

cd src/finetune
sh train.sh 4 [data]

Uncertainty Expression

cd src/finetune
sh scripts/trivia_qa/pe_pipeline_llama2-chat-7b.sh [lora_weights]

Citation

@inproceedings{liu-etal-2024-llms-learn-uncertainty,
    title = "Can {LLM}s Learn Uncertainty on Their Own? Expressing Uncertainty Effectively in A Self-Training Manner",
    author = "Liu, Shudong  and
      Li, Zhaocong  and
      Liu, Xuebo  and
      Zhan, Runzhe  and
      Wong, Derek  and
      Chao, Lidia  and
      Zhang, Min",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.1205",
    pages = "21635--21645",
}