Official resources of "ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models". Haoran Luo, Haihong E, Zichen Tang, Shiyao Peng, Yikai Guo, Wentai Zhang, Chenghao Ma, Guanting Dong, Meina Song, Wei Lin, Yifan Zhu, Luu Anh Tuan. Findings of ACL 2024 [paper].
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
Below steps are according to Freebase Virtuoso Setup.
- Clone from
dki-lab/Freebase-Setup:
cd Freebase-Setup
- Processed Freebase Virtuoso DB file can be downloaded from Dropbox or Baidu Netdisk (WARNING: 53G+ disk space is needed):
tar -zxvf virtuoso_db.zip
- Managing the Virtuoso service:
To start service at localhost:3001/sparql:
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 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
Experiments are conducted on 2 KBQA benchmarks WebQSP, CWQ.
WebQSP dataset has been downloaded under data/WebQSP/origin.
ChatKBQA/
└── data/
├── WebQSP
├── origin
├── WebQSP.train.json
└── WebQSP.test.json
CWQ dataset has been downloaded under data/CWQ/origin.
ChatKBQA/
└── data/
├── CWQ
├── origin
├── ComplexWebQuestions_train.json
├── ComplexWebQuestions_dev.json
└── ComplexWebQuestions_test.json
(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.
Note: You can also get the ChatKBQA processed data from TeraBox or Baidu Netdisk, which should be set in data/.
ChatKBQA/
└── data/
├── CWQ/
├── generation/
├── origin/
└── sexpr/
└── WebQSP/
├── generation/
├── origin/
└── sexpr/
(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.
Note: You can also get the processed ChatKBQA SFT data from TeraBox or Baidu Netdisk, which should be set in LLMs/data.
ChatKBQA/
└── LLMs/
├── data/
├── CWQ_Freebase_NQ_test/
├── CWQ_Freebase_NQ_train/
├── WebQSP_Freebase_NQ_test/
├── WebQSP_Freebase_NQ_train/
└── dataset_info.json
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.
(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 &Note: You can also get the ChatKBQA checkpoints and evaluations from TeraBox or Baidu Netdisk, which should be set in Reading/.
ChatKBQA/
└── Reading/
├── LLaMA2-7b/
└── WebQSP_Freebase_NQ_lora_epoch100/
├── checkpoint/
└── evaluation_beam/
└── LLaMA2-13b/
└── CWQ_Freebase_NQ_lora_epoch10/
├── checkpoint/
└── evaluation_beam/
If you find this work is helpful for your research, please cite:
@inproceedings{luo2024chatkbqa,
title = "{C}hat{KBQA}: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models",
author = "Luo, Haoran and
E, Haihong and
Tang, Zichen and
Peng, Shiyao and
Guo, Yikai and
Zhang, Wentai and
Ma, Chenghao and
Dong, Guanting and
Song, Meina and
Lin, Wei and
Zhu, Yifan and
Luu, Anh Tuan",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.122",
pages = "2039--2056",
abstract = "Knowledge Base Question Answering (KBQA) aims to answer natural language questions over large-scale knowledge bases (KBs), which can be summarized into two crucial steps: knowledge retrieval and semantic parsing. However, three core challenges remain: inefficient knowledge retrieval, mistakes of retrieval adversely impacting semantic parsing, and the complexity of previous KBQA methods. To tackle these challenges, we introduce ChatKBQA, a novel and simple generate-then-retrieve KBQA framework, which proposes first generating the logical form with fine-tuned LLMs, then retrieving and replacing entities and relations with an unsupervised retrieval method, to improve both generation and retrieval more directly. Experimental results show that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets, WebQSP, and CWQ. This work can also be regarded as a new paradigm for combining LLMs with knowledge graphs (KGs) for interpretable and knowledge-required question answering.",
}For further questions, please contact: luohaoran@bupt.edu.cn.
This repo benefits from PEFT, LLaMA-Efficient-Tuning, SimCSE, GMT-KBQA and DECAF. Thanks for their wonderful works.