@inproceedings{thapa-etal-2023-assessing,
title = "Assessing Political Inclination of {B}angla Language Models",
author = "Thapa, Surendrabikram and
Maratha, Ashwarya and
Hasib, Khan Md and
Nasim, Mehwish and
Naseem, Usman",
editor = "Alam, Firoj and
Kar, Sudipta and
Chowdhury, Shammur Absar and
Sadeque, Farig and
Amin, Ruhul",
booktitle = "Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.banglalp-1.8/",
doi = "10.18653/v1/2023.banglalp-1.8",
pages = "62--71",
abstract = "Natural language processing has advanced with AI-driven language models (LMs), that are applied widely from text generation to question answering. These models are pre-trained on a wide spectrum of data sources, enhancing accuracy and responsiveness. However, this process inadvertently entails the absorption of a diverse spectrum of viewpoints inherent within the training data. Exploring political leaning within LMs due to such viewpoints remains a less-explored domain. In the context of a low-resource language like Bangla, this area of research is nearly non-existent. To bridge this gap, we comprehensively analyze biases present in Bangla language models, specifically focusing on social and economic dimensions. Our findings reveal the inclinations of various LMs, which will provide insights into ethical considerations and limitations associated with deploying Bangla LMs."
}
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%0 Conference Proceedings
%T Assessing Political Inclination of Bangla Language Models
%A Thapa, Surendrabikram
%A Maratha, Ashwarya
%A Hasib, Khan Md
%A Nasim, Mehwish
%A Naseem, Usman
%Y Alam, Firoj
%Y Kar, Sudipta
%Y Chowdhury, Shammur Absar
%Y Sadeque, Farig
%Y Amin, Ruhul
%S Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F thapa-etal-2023-assessing
%X Natural language processing has advanced with AI-driven language models (LMs), that are applied widely from text generation to question answering. These models are pre-trained on a wide spectrum of data sources, enhancing accuracy and responsiveness. However, this process inadvertently entails the absorption of a diverse spectrum of viewpoints inherent within the training data. Exploring political leaning within LMs due to such viewpoints remains a less-explored domain. In the context of a low-resource language like Bangla, this area of research is nearly non-existent. To bridge this gap, we comprehensively analyze biases present in Bangla language models, specifically focusing on social and economic dimensions. Our findings reveal the inclinations of various LMs, which will provide insights into ethical considerations and limitations associated with deploying Bangla LMs.
%R 10.18653/v1/2023.banglalp-1.8
%U https://aclanthology.org/2023.banglalp-1.8/
%U https://doi.org/10.18653/v1/2023.banglalp-1.8
%P 62-71
Markdown (Informal)
[Assessing Political Inclination of Bangla Language Models](https://aclanthology.org/2023.banglalp-1.8/) (Thapa et al., BanglaLP 2023)
ACL
- Surendrabikram Thapa, Ashwarya Maratha, Khan Md Hasib, Mehwish Nasim, and Usman Naseem. 2023. Assessing Political Inclination of Bangla Language Models. In Proceedings of the First Workshop on Bangla Language Processing (BLP-2023), pages 62–71, Singapore. Association for Computational Linguistics.