ChatKBQA

ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models

Overview

General Setup

Environment Setup

conda create -n chatkbqa python=3.8
conda activate chatkbqa
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
pip install -r requirement.txt

Freebase KG Setup

Below steps are according to Freebase Virtuoso Setup.

How to install virtuoso backend for Freebase KG.

1. Clone from dki-lab/Freebase-Setup:

cd Freebase-Setup

2. Processed Freebase Virtuoso DB file can be downloaded from Dropbox or Baidu Netdisk (WARNING: 53G+ disk space is needed):

tar -zxvf virtuoso_db.zip

3. Managing the Virtuoso service:

To start service:

python3 virtuoso.py start 3001 -d virtuoso_db

and to stop a currently running service at the same port:

python3 virtuoso.py stop 3001

A server with at least 100 GB RAM is recommended.

Download FACC1 mentions for Entity Retrieval.

• Download the mention information (including processed FACC1 mentions and all entity alias in Freebase) from OneDrive or Baidu Netdisk to data/common_data/facc1/.

ChatKBQA/
└── data/
    ├── common_data/                  
        ├── facc1/   
            ├── entity_list_file_freebase_complete_all_mention
            └── surface_map_file_freebase_complete_all_mention                                           

Dataset

Experiments are conducted on 2 KBQA benchmarks WebQSP, CWQ.

WebQSP

WebQSP dataset has been downloaded under data/WebQSP/origin.

ChatKBQA/
└── data/
    ├── WebQSP                  
        ├── origin                    
            ├── WebQSP.train.json                    
            └── WebQSP.test.json                                       

CWQ

CWQ dataset has been downloaded under data/CWQ/origin.

ChatKBQA/
└── data/
    ├── CWQ                 
        ├── origin                    
            ├── ComplexWebQuestions_train.json                   
            ├── ComplexWebQuestions_dev.json      
            └── ComplexWebQuestions_test.json                              

Data Processing

(1) Parse SPARQL queries to S-expressions

• WebQSP:

Run python parse_sparql_webqsp.py and the augmented dataset files are saved as data/WebQSP/sexpr/WebQSP.test[train].json.

• CWQ:

Run python parse_sparql_cwq.py and the augmented dataset files are saved as data/CWQ/sexpr/CWQ.test[train].json.

(2) Prepare data for training and evaluation

• WebQSP:

Run python data_process.py --action merge_all --dataset WebQSP --split test and python data_process.py --action merge_all --dataset WebQSP --split train. The merged data file will be saved as data/WebQSP/generation/merged/WebQSP_test[train].json.

Run python data_process.py --action get_type_label_map --dataset WebQSP --split train. The merged data file will be saved as data/WebQSP/generation/label_maps/WebQSP_train_type_label_map.json.

• CWQ:

Run python data_process.py --action merge_all --dataset CWQ --split test and python data_process.py --action merge_all --dataset CWQ --split train. The merged data file will be saved as data/CWQ/generation/merged/CWQ_test[train].json.

Run python data_process.py --action get_type_label_map --dataset CWQ --split train. The merged data file will be saved as data/CWQ/generation/label_maps/CWQ_train_type_label_map.json.

(3) Prepare data for LLM model

• WebQSP:

Run python process_NQ.py --dataset_type WebQSP. The merged data file will be saved as LLMs/data/WebQSP_Freebase_NQ_test[train]/examples.json.

• CWQ:

Run python process_NQ.py --dataset_type CWQ The merged data file will be saved as LLMs/data/CWQ_Freebase_NQ_test[train]/examples.json.

Fine-tuning, Retrieval and Evaluation

The following is an example of LLaMa2-7b fine-tuning and retrieval (num_beam = 15) on WebQSP and LLaMa2-13b fine-tuning and retrieval (num_beam = 8) on CWQ, respectively. We also provide a variety of LLMs (including LLaMa2-7b, LLaMa2-13b, ChatGLM2-6b, Baichuan2-7b, Baichuan2-13b) fine-tuning scripts (num_beam = 8).

(1) Train and test LLM model for Logical Form Generation

• WebQSP:

Train LLMs for Logical Form Generation:

CUDA_VISIBLE_DEVICES=3 nohup python -u LLMs/LLaMA/src/train_bash.py --stage sft --model_name_or_path meta-llama/Llama-2-7b-hf --do_train  --dataset_dir LLMs/data --dataset WebQSP_Freebase_NQ_train --template llama2  --finetuning_type lora --lora_target q_proj,v_proj --output_dir Reading/LLaMA2-7b/WebQSP_Freebase_NQ_lora_epoch100/checkpoint --overwrite_cache --per_device_train_batch_size 4 --gradient_accumulation_steps 4  --lr_scheduler_type cosine --logging_steps 10 --save_steps 1000 --learning_rate 5e-5  --num_train_epochs 100.0 --plot_loss  --fp16 >> train_LLaMA2-7b_WebQSP_Freebase_NQ_lora_epoch100.txt 2>&1 &

Beam-setting LLMs for Logical Form Generation:

CUDA_VISIBLE_DEVICES=3 nohup python -u LLMs/LLaMA/src/beam_output_eva.py --model_name_or_path meta-llama/Llama-2-7b-hf --dataset_dir LLMs/data --dataset WebQSP_Freebase_NQ_test --template llama2 --finetuning_type lora --checkpoint_dir Reading/LLaMA2-7b/WebQSP_Freebase_NQ_lora_epoch100/checkpoint --num_beams 15 >> predbeam_LLaMA2-7b_WebQSP_Freebase_NQ_lora_epoch100.txt 2>&1 &

python run_generator_final.py --data_file_name Reading/LLaMA2-7b/WebQSP_Freebase_NQ_lora_epoch100/evaluation_beam/generated_predictions.jsonl

• CWQ:

Train LLMs for Logical Form Generation:

CUDA_VISIBLE_DEVICES=2 nohup python -u LLMs/LLaMA/src/train_bash.py --stage sft --model_name_or_path meta-llama/Llama-2-13b-hf --do_train  --dataset_dir LLMs/data --dataset CWQ_Freebase_NQ_train --template default  --finetuning_type lora --lora_target q_proj,v_proj --output_dir Reading/LLaMA2-13b/CWQ_Freebase_NQ_lora_epoch10/checkpoint --overwrite_cache --per_device_train_batch_size 4 --gradient_accumulation_steps 4  --lr_scheduler_type cosine --logging_steps 10 --save_steps 1000 --learning_rate 5e-5  --num_train_epochs 10.0 --plot_loss  --fp16 >> train_LLaMA2-13b_CWQ_Freebase_NQ_lora_epoch10.txt 2>&1 &

Beam-setting LLMs for Logical Form Generation:

CUDA_VISIBLE_DEVICES=3 nohup python -u LLMs/LLaMA/src/beam_output_eva.py --model_name_or_path meta-llama/Llama-2-13b-hf --dataset_dir LLMs/data --dataset CWQ_Freebase_NQ_test --template default --finetuning_type lora --checkpoint_dir Reading/LLaMA2-13b/CWQ_Freebase_NQ_lora_epoch10/checkpoint --num_beams 8 >> predbeam_LLaMA2-13b_CWQ_Freebase_NQ_lora_epoch10.txt 2>&1 &

python run_generator_final.py --data_file_name Reading/LLaMA2-13b/CWQ_Freebase_NQ_lora_epoch10/evaluation_beam/generated_predictions.jsonl

(2) Evaluate KBQA result with Retrieval

• WebQSP:

Evaluate KBQA result with entity-retrieval and relation-retrieval:

CUDA_VISIBLE_DEVICES=1 nohup python -u eval_final.py --dataset WebQSP --pred_file Reading/LLaMA2-7b/WebQSP_Freebase_NQ_lora_epoch100/evaluation_beam/beam_test_top_k_predictions.json >> predfinal_LLaMA2-7b_WebQSP_Freebase_NQ_lora_epoch100.txt 2>&1 &

Evaluate KBQA result with golden-entities and relation-retrieval:

CUDA_VISIBLE_DEVICES=4 nohup python -u eval_final.py --dataset WebQSP --pred_file Reading/LLaMA2-7b/WebQSP_Freebase_NQ_lora_epoch100/evaluation_beam/beam_test_top_k_predictions.json --golden_ent >> predfinalgoldent_LLaMA2-7b_WebQSP_Freebase_NQ_lora_epoch100.txt 2>&1 &

• CWQ:

Evaluate KBQA result with entity-retrieval and relation-retrieval:

CUDA_VISIBLE_DEVICES=4 nohup python -u eval_final_cwq.py --dataset CWQ --pred_file Reading/LLaMA2-13b/CWQ_Freebase_NQ_lora_epoch10/evaluation_beam/beam_test_top_k_predictions.json >> predfinal_LLaMA2-13b_CWQ_Freebase_NQ_lora_epoch10.txt 2>&1 &

Evaluate KBQA result with golden-entities and relation-retrieval:

CUDA_VISIBLE_DEVICES=5 nohup python -u eval_final_cwq.py --dataset CWQ --pred_file Reading/LLaMA2-13b/CWQ_Freebase_NQ_lora_epoch10/evaluation_beam/beam_test_top_k_predictions.json --golden_ent >> predfinalgoldent_LLaMA2-13b_CWQ_Freebase_NQ_lora_epoch10.txt 2>&1 &

BibTex

If you find this work is helpful for your research, please cite:

@misc{luo2023chatkbqa,
      title={ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models}, 
      author={Haoran Luo and Haihong E and Zichen Tang and Shiyao Peng and Yikai Guo and Wentai Zhang and Chenghao Ma and Guanting Dong and Meina Song and Wei Lin},
      year={2023},
      eprint={2310.08975},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

For further questions, please contact: [email protected], or wechat: lhr1846205978.

Acknowledgement

This repo benefits from PEFT, LLaMA-Efficient-Tuning, SimCSE, GMT-KBQA and DECAF. Thanks for their wonderful works.