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Model: Value4AI/ValueLlama-3-8B
Source: Original Platform

README.md

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+---
+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}, 
+}
+```
+