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# SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models | |
In order to run selfcheckgpt evaluation, these dependencies should be installed. | |
``` | |
pip install spacy | |
pip install selfcheckgpt | |
python -m spacy download en | |
``` | |
selfcheckgpt support different evaluation methods including: `SelfCheckNgram`, `SelfCheckBERTScore`, `SelfCheckMQAG` and `SelfCheckNLI`. | |
The default evaluation method in llm-eval-harness is `SelfCheckNgram`. You can change the evaluation method by changing the environment variable | |
``` | |
export SELFCHECKGPTTYPE=SelfCheckNgram | |
``` | |
For `SelfCheckBERTScore`, `SelfCheckMQAG` and `SelfCheckNLI` evaluation method which will also run some huggingface models, You can change the running device of the selfcheckgpt to GPU by setting enviroment device: | |
``` | |
export SELFCHECKGPTDEVICE=cuda | |
``` | |
## Citation | |
``` | |
@misc{manakul2023selfcheckgpt, | |
title={SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models}, | |
author={Potsawee Manakul and Adian Liusie and Mark J. F. Gales}, | |
year={2023}, | |
eprint={2303.08896}, | |
archivePrefix={arXiv}, | |
primaryClass={cs.CL} | |
} | |
``` |