Summary of Estimating Causal Effects Of Text Interventions Leveraging Llms, by Siyi Guo et al.
Estimating Causal Effects of Text Interventions Leveraging LLMs
by Siyi Guo, Myrl G. Marmarelis, Fred Morstatter, Kristina Lerman
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes CausalDANN, a novel approach to estimate causal effects using large language models (LLMs) on textual data. The method accommodates arbitrary textual interventions and leverages text-level classifiers with domain adaptation ability to produce robust effect estimates against domain shifts. This flexibility in handling various text interventions is a key advancement in causal estimation for textual data, offering opportunities to better understand human behaviors and develop effective policies within social systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how changing what people write on social media can affect engagement. It’s hard to study this because we can’t always control the things people post. The researchers created a new method called CausalDANN that uses large language models to figure out how changes in text impact engagement. This method is special because it works even when we only have data from one group and not both groups. This can help us learn more about human behavior and create better policies for social media. |
Keywords
» Artificial intelligence » Domain adaptation
