Awesome

Know the Unknown

Code for paper: Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning

Datasets and Benchmark

Our datasets and benchmark base on ASQA and HotpotQA. For generating our proposed datasets, you need to refer to the scripts in datasets directory, and download the original datasets from the above links.

We provide the processed data and benchmark for our experiments. The data and benchmark are available at Onedrive. Our the generated outputs of mainstream LLMs on our benchmark are also available at Onedrive.

Pretrained Models

We provide the pretrained models of our fine-tuned models at Onedrive. Those modes ended with merged are the entire weights merged with the original weights, while the others are just Lora heads.

Training

We fork the code from LLaMA-Factory to achieve the fine-tuning. As our proposed method is a two-stage framework, you need to first fine-tune the LLMs on the TrustworthyLLM_Cognition_Finetuning_Dataset, then fine-tune it on the TrustworthyLLM_PromptSensitive_Finetuning_Dataset. Here is an example of the command for fine-tuning on the TrustworthyLLM_Cognition_Finetuning_Dataset:

python LLaMA-Factory/src/train_bash.py
--stage
sft
--do_train
--model_name_or_path
/mnt/f/Models/llama-2-7b-chat-hf
--create_new_adapter
--dataset
TrustworthyLLM_Cognition_Finetuning_Dataset
--template
llama2
--finetuning_type
lora
--lora_target
q_proj,v_proj
--output_dir
models/TrustworthyLLM_Cognition_Finetuning_Model
--overwrite_cache
--per_device_train_batch_size
4
--gradient_accumulation_steps
4
--lr_scheduler_type
cosine
--logging_steps
10
--save_steps
1000
--learning_rate
4e-5
--num_train_epochs
1.0
--plot_loss
--fp16