Aligning Large Language Models with Diverse Political Viewpoints
CoRR(2024)
Abstract
Large language models such as ChatGPT often exhibit striking political
biases. If users query them about political information, they might take a
normative stance and reinforce such biases. To overcome this, we align LLMs
with diverse political viewpoints from 100,000 comments written by candidates
running for national parliament in Switzerland. Such aligned models are able to
generate more accurate political viewpoints from Swiss parties compared to
commercial models such as ChatGPT. We also propose a procedure to generate
balanced overviews from multiple viewpoints using such models.
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