53 lines
1.7 KiB
Markdown
53 lines
1.7 KiB
Markdown
---
|
|
library_name: transformers
|
|
tags:
|
|
- llama-factory
|
|
license: llama3
|
|
datasets:
|
|
- allenai/ValuePrism
|
|
- Value4AI/ValueBench
|
|
language:
|
|
- en
|
|
---
|
|
|
|
# Model Card for ValueLlama
|
|
|
|
|
|
## Model Description
|
|
|
|
|
|
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.
|
|
|
|
- **Model type:** Language model
|
|
- **Language(s) (NLP):** en
|
|
- **Finetuned from model:** [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
|
|
|
|
## Paper
|
|
|
|
|
|
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).
|
|
|
|
## Uses
|
|
|
|
It is intended for use in **research** to measure human/AI values and conduct related analyses.
|
|
|
|
See our codebase for more details: [https://github.com/Value4AI/gpv](https://github.com/Value4AI/gpv).
|
|
|
|
|
|
## BibTeX:
|
|
|
|
If you find this model helpful, we would appreciate it if you cite our paper:
|
|
|
|
```bibtex
|
|
@misc{ye2024gpv,
|
|
title={Measuring Human and AI Values based on Generative Psychometrics with Large Language Models},
|
|
author={Haoran Ye and Yuhang Xie and Yuanyi Ren and Hanjun Fang and Xin Zhang and Guojie Song},
|
|
year={2024},
|
|
eprint={2409.12106},
|
|
archivePrefix={arXiv},
|
|
primaryClass={cs.CL},
|
|
url={https://arxiv.org/abs/2409.12106},
|
|
}
|
|
```
|
|
|