Papers by Rebecca Dorn
Community-Cross-Instruct: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities (2024.emnlp-main)
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| Challenge: | Social scientists use surveys to learn opinions and beliefs of populations, but these methods are slow, costly, and prone to biases. |
| Approach: | They propose a framework for aligning large language models to online communities by finetuning instruction-output pairs by an advanced LLM to elicit their beliefs. |
| Outcome: | The proposed framework enables cost-effective and automated surveying of diverse online communities. |
Improving and Assessing the Fidelity of Large Language Models Alignment to Online Communities (2025.naacl-long)
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| Challenge: | Large language models (LLMs) have shown promise in representing individuals and communities, but evaluating their fidelity remains a challenge. |
| Approach: | They propose a framework for aligning large language models with online communities via instruction-tuning and comprehensively evaluating alignment across various aspects of language. |
| Outcome: | The proposed framework shows that it can be used to create high-fidelity representations of people and communities. |
OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants (2024.emnlp-main)
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Jaspreet Ranjit, Brihi Joshi, Rebecca Dorn, Laura Petry, Olga Koumoundouros, Jayne Bottarini, Peichen Liu, Eric Rice, Swabha Swayamdipta
| Challenge: | a large-scale analysis of millions of tweets on homelessness is challenging to understand at scale. |
| Approach: | They propose a framing typology: Online Attitudes Towards Homelessness (OATH) They use large language models to analyze millions of tweets to find patterns in public attitudes . |
| Outcome: | The proposed model speeds up annotations while incurring a 3 point performance reduction compared to existing classifiers . |