Papers by Ashwin Rao

2 papers
Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities (2024.findings-eacl)

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Challenge: Existing studies treat ideology as a liberal/conservative binary and fail to capture the spectrum of ideologies that may organically emerge in interconnected online communities.
Approach: They propose a method that uses finetuning language models to probe nuanced ideologies of online communities by analyzing discussions of the 2020 election on Twitter.
Outcome: The proposed approach shows higher alignment than baselines for the proposed approach.
Whose Emotions and Moral Sentiments do Language Models Reflect? (2024.findings-acl)

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Challenge: Existing research has focused on positional alignment, which measures how closely the models mimic the opinions and stances of different social groups.
Approach: They define the problem of affective alignment, which measures how LMs’ emotional and moral tone represents those of different groups.
Outcome: The results show that the models represent the perspectives of some social groups better than others, suggesting a systemic bias within LMs.

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