62 citations · 197 across the 9 of their papers we have counts for
9 papers
Embedding Democratic Values into Social Media AIs via Societal Objective Functions
Chenyan Jia, Michelle S. Lam, Minh Chau Mai +2
Can we design artificial intelligence (AI) systems that rank our social media feeds to consider democratic values such as mitigating partisan animosity as part of their objective f…
Training Socially Aligned Language Models on Simulated Social Interactions
Ruibo Liu, Ruixin Yang, Chenyan Jia +5
Social alignment in AI systems aims to ensure that these models behave according to established societal values. However, unlike humans, who derive consensus on value judgments thr…
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits
Ruibo Liu, Chenyan Jia, Ge Zhang +3
We present Second Thought, a new learning paradigm that enables language models (LMs) to re-align with human values. By modeling the chain-of-edits between value-unaligned and valu…
Non-Parallel Text Style Transfer with Self-Parallel Supervision
Ruibo Liu, Chongyang Gao, Chenyan Jia +2
The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained. In non-parallel datasets, no direct mapping…
Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation
Chenyan Jia, Alexander Boltz, Angie Zhang +2
Hyper-partisan misinformation has become a major public concern. In order to examine what type of misinformation label can mitigate hyper-partisan misinformation sharing on social…
Modulating Language Models with Emotions
Ruibo Liu, Jason Wei, Chenyan Jia +1
Generating context-aware language that embodies diverse emotions is an important step towards building empathetic NLP systems. In this paper, we propose a formulation of modulated…
Mitigating Political Bias in Language Models Through Reinforced Calibration
Ruibo Liu, Chenyan Jia, Jason Wei +3
Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world…
Political Depolarization of News Articles Using Attribute-aware Word Embeddings
Ruibo Liu, Lili Wang, Chenyan Jia +1
Political polarization in the US is on the rise. This polarization negatively affects the public sphere by contributing to the creation of ideological echo chambers. In this paper,…
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation
Ruibo Liu, Guangxuan Xu, Chenyan Jia +3
Data augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text augme…