The Language Model Can Have the Personality: Joint Learning for Personality Enhanced Language Model (Student Abstract)
Abstract
With the introduction of large language models, chatbots are becoming more conversational to communicate effectively and capable of handling increasingly complex tasks. To make a chatbot more relatable and engaging, we propose a new language model idea that maps the human-like personality. In this paper, we propose a systematic Personality-Enhanced Language Model (PELM) approach by using a joint learning mechanism of personality classification and language generation tasks. The proposed PELM leverages a dataset of defined personality typology, Myers-Briggs Type Indicator, and produces a Personality-Enhanced Language Model by using a joint learning and cross-teaching structure consisting of a classification and language modelling to incorporate personalities via both distinctive types and textual information. The results show that PELM can generate better personality-based outputs than baseline models.
Cite
Text
Chen et al. "The Language Model Can Have the Personality: Joint Learning for Personality Enhanced Language Model (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30426Markdown
[Chen et al. "The Language Model Can Have the Personality: Joint Learning for Personality Enhanced Language Model (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/chen2024aaai-language/) doi:10.1609/AAAI.V38I21.30426BibTeX
@inproceedings{chen2024aaai-language,
title = {{The Language Model Can Have the Personality: Joint Learning for Personality Enhanced Language Model (Student Abstract)}},
author = {Chen, Tianyi and Cao, Feiqi and Ding, Yihao and Han, Soyeon Caren},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23454-23455},
doi = {10.1609/AAAI.V38I21.30426},
url = {https://mlanthology.org/aaai/2024/chen2024aaai-language/}
}