no code implementations • 11 Apr 2024 • Samuel Cahyawijaya, Delong Chen, Yejin Bang, Leila Khalatbari, Bryan Wilie, Ziwei Ji, Etsuko Ishii, Pascale Fung
there is an urgent need to understand the scope and nature of human values injected into these models before their release.
no code implementations • 27 Mar 2024 • Yejin Bang, Delong Chen, Nayeon Lee, Pascale Fung
We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues.
no code implementations • 3 Nov 2023 • Yejin Bang, Nayeon Lee, Pascale Fung
Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events.
no code implementations • 24 Sep 2023 • Nayeon Lee, Yejin Bang, Holy Lovenia, Samuel Cahyawijaya, Wenliang Dai, Pascale Fung
This survey aims to provide researchers with a high-level insight into the similarities and differences of social bias studies in pre-trained models across NLP, CV, and VL.
no code implementations • 21 Apr 2023 • Leila Khalatbari, Yejin Bang, Dan Su, Willy Chung, Saeed Ghadimi, Hossein Sameti, Pascale Fung
Our approach differs from the standard contrastive learning framework in that it automatically obtains positive and negative signals from the safe and unsafe language distributions that have been learned beforehand.
1 code implementation • 8 Feb 2023 • Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, Quyet V. Do, Yan Xu, Pascale Fung
It is, for example, better at deductive than inductive reasoning.
no code implementations • 10 Nov 2022 • Caner Hazirbas, Yejin Bang, Tiezheng Yu, Parisa Assar, Bilal Porgali, Vítor Albiero, Stefan Hermanek, Jacqueline Pan, Emily McReynolds, Miranda Bogen, Pascale Fung, Cristian Canton Ferrer
Developing robust and fair AI systems require datasets with comprehensive set of labels that can help ensure the validity and legitimacy of relevant measurements.
no code implementations • 14 Oct 2022 • Yejin Bang, Tiezheng Yu, Andrea Madotto, Zhaojiang Lin, Mona Diab, Pascale Fung
Therefore, we introduce a framework for value-aligned classification that performs prediction based on explicitly written human values in the command.
no code implementations • 12 May 2022 • Yejin Bang, Nayeon Lee, Tiezheng Yu, Leila Khalatbari, Yan Xu, Samuel Cahyawijaya, Dan Su, Bryan Wilie, Romain Barraud, Elham J. Barezi, Andrea Madotto, Hayden Kee, Pascale Fung
We explore the current capability of LLMs in providing an answer with a deliberative exchange of different perspectives to an ethical quandary, in the approach of Socratic philosophy, instead of providing a closed answer like an oracle.
1 code implementation • NAACL 2022 • Nayeon Lee, Yejin Bang, Tiezheng Yu, Andrea Madotto, Pascale Fung
Based on our discovery that title provides a good signal for framing bias, we present NeuS-TITLE that learns to neutralize news content in hierarchical order from title to article.
no code implementations • 8 Feb 2022 • Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Delong Chen, Ho Shu Chan, Wenliang Dai, Andrea Madotto, Pascale Fung
This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation.
1 code implementation • SIGDIAL (ACL) 2021 • Yejin Bang, Nayeon Lee, Etsuko Ishii, Andrea Madotto, Pascale Fung
In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence.
no code implementations • 23 Apr 2021 • Wenliang Dai, Samuel Cahyawijaya, Yejin Bang, Pascale Fung
In this paper, we propose to leverage these datasets using weakly-supervised multi-task learning to improve the generalization performance on each of them.
no code implementations • 18 Apr 2021 • Nayeon Lee, Andrea Madotto, Yejin Bang, Pascale Fung
Rumors are often associated with newly emerging events, thus, an ability to deal with unseen rumors is crucial for a rumor veracity classification model.
no code implementations • 1 Apr 2021 • Nayeon Lee, Yejin Bang, Andrea Madotto, Pascale Fung
Media bias can lead to increased political polarization, and thus, the need for automatic mitigation methods is growing.
no code implementations • NAACL 2021 • Nayeon Lee, Yejin Bang, Andrea Madotto, Madian Khabsa, Pascale Fung
Through experiments, we empirically verify the plausibility of the rather surprising usage of the perplexity score in the context of fact-checking and highlight the strength of our few-shot methodology by comparing it to strong fine-tuning-based baseline models.
no code implementations • 11 Jan 2021 • Yejin Bang, Etsuko Ishii, Samuel Cahyawijaya, Ziwei Ji, Pascale Fung
Amid the pandemic COVID-19, the world is facing unprecedented infodemic with the proliferation of both fake and real information.
1 code implementation • 28 Aug 2020 • Andrea Madotto, Zhaojiang Lin, Yejin Bang, Pascale Fung
The dialogue skills can be triggered automatically via a dialogue manager, or manually, thus allowing high-level control of the generated responses.
1 code implementation • 8 Jun 2020 • Nayeon Lee, Yejin Bang, Andrea Madotto, Pascale Fung
Debunking misinformation is an important and time-critical task as there could be adverse consequences when misinformation is not quashed promptly.
1 code implementation • EMNLP (NLP4ConvAI) 2021 • Zhaojiang Lin, Zihan Liu, Genta Indra Winata, Samuel Cahyawijaya, Andrea Madotto, Yejin Bang, Etsuko Ishii, Pascale Fung
Experimental results show that the multilingual trained models outperform the translation-pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages.
no code implementations • WS 2019 • Nayeon Lee, Yejin Bang, Jamin Shin, Pascale Fung
[Multiple-submission] In the midst of a generation widely exposed to and influenced by media entertainment, the NLP research community has shown relatively little attention on the sexist comments in popular TV series.