2024
EMNLP
EMNLP 2024
Aligning Large Language Models with Diverse Political Viewpoints
Abstract
AbstractLarge language models such as ChatGPT exhibit striking political biases. If users query them about political information, they often take a normative stance. To overcome this, we align LLMs with diverse political viewpoints from 100,000 comments written by candidates running for national parliament in Switzerland. Models aligned with this data can generate more accurate political viewpoints from Swiss parties, compared to commercial models such as ChatGPT. We also propose a procedure to generate balanced overviews summarizing multiple viewpoints using such models. The replication package contains all code and data.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— swiss politics
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Hot Topic Early Bird
— political bia
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Responsible AI
Machine Learning > Application Areas > Fairness
Natural Language Processing > Resources & Methods > Large Language Models
Machine Learning > Learning Types > Transfer Learning
Artificial Intelligence > Core AI > Large Language Models
Artificial Intelligence > Core AI > Fairness
Deep Learning > Models > Large Language Models
Deep Learning > Learning Types > Fine-Tuning