You're currently viewing an old version of this dataset. To see the current version, click here.

Latent Distance Guided Alignment Training for Large Language Models

Ensuring alignment with human preferences is a crucial characteristic of large language models (LLMs). Presently, the primary alignment methods, RLHF and DPO, require extensive human annotation, which is expensive despite their efficacy.

Data and Resources

Cite this as

Haotian Luo (2024). Dataset: Latent Distance Guided Alignment Training for Large Language Models. https://doi.org/10.57702/8jb217g1

DOI retrieved: December 16, 2024

Additional Info

Field Value
Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.2404.06390
Author Haotian Luo
Homepage https://arxiv.org/abs/2309.00267