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ragarwal/factual-consistency-llama3-8b
factual-consistency-llama3-8b is a machine learning model from ragarwal. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as llama3.
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From the Hugging Face model README
Inference can be done using VLLM
Data:
| Method | Rank | Mean Win Rate (%) | Average AUC |
|---|---|---|---|
| Llama-3-8B (FT) (Ours) | 1 | 78.11 | 78.037 |
| Flan-T5-L (FT) (Ours) | 2 | 76.43 | 78.663 |
| MiniCheck-T5-L | 3 | 72.39 | 76.674 |
| gpt-3.5-turbo | 4 | 69.36 | 77.007 |
| Flan-T5-B (FT) (Ours) | 5 | 66.00 | 76.126 |
| AlignScore-L | 6 | 53.19 | 73.074 |
| Llama-3-8B | 7 | 53.20 | 75.085 |
| AlignScore-B | 8 | 39.39 | 71.319 |
| QuestEval | 9 | 37.37 | 66.089 |
| BARTScore | 10 | 26.94 | 62.637 |
| BERTScore | 11 | 20.88 | 61.263 |
| ROUGE-L | 12 | 6.73 | 54.678 |
Comparison of different factuality evaluation methods across all test datasets. The methods are ranked based on the Mean Win Rate, which measures overall performance on factuality tasks. The Average AUC column represents the average of all individual AUC-ROC scores.
Cite this work as follows:
@misc{agarwal2024zeroshotfactualconsistencyevaluation,
title={Zero-shot Factual Consistency Evaluation Across Domains},
author={Raunak Agarwal},
year={2024},
eprint={2408.04114},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2408.04114},
}