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--- |
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library_name: transformers |
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tags: |
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- llama-factory |
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license: llama3 |
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datasets: |
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- allenai/ValuePrism |
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- Value4AI/ValueBench |
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language: |
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- en |
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--- |
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# Model Card for ValueLlama |
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## Model Description |
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ValueLlama is designed for perception-level value measurement in an open-ended value space, which includes two tasks: (1) Relevance classification determines whether a perception is relevant to a value; and (2) Valence classification determines whether a perception supports, opposes, or remains neutral (context-dependent) towards a value. Both tasks are formulated as generating a label given a value and a perception. |
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- **Model type:** Language model |
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- **Language(s) (NLP):** en |
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- **Finetuned from model:** [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) |
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## Paper |
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For more information, please refer to our paper: [*Measuring Human and AI Values based on Generative Psychometrics with Large Language Models*](https://arxiv.org/abs/2409.12106). |
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## Uses |
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It is intended for use in **research** to measure human/AI values and conduct related analyses. |
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See our codebase for more details: [https://github.com/Value4AI/gpv](https://github.com/Value4AI/gpv). |
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## BibTeX: |
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If you find this model helpful, we would appreciate it if you cite our paper: |
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```bibtex |
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@misc{ye2024gpv, |
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title={Measuring Human and AI Values based on Generative Psychometrics with Large Language Models}, |
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author={Haoran Ye and Yuhang Xie and Yuanyi Ren and Hanjun Fang and Xin Zhang and Guojie Song}, |
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year={2024}, |
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eprint={2409.12106}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2409.12106}, |
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} |
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``` |
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