Papers by Kristina Gligorić
Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectification (2026.acl-long)
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| Challenge: | Large language models (LLMs) are a cost-effective and time-consuming way to capture public opinion and behavior, but their outputs are often biased and yield invalid estimates. |
| Approach: | They propose to use large language models to generate survey responses and rectification methods that debias population estimates to find out how human responses are best allocated between them. |
| Outcome: | The proposed methods reduce bias below 5% and increase sample size by up to 14% under a fixed budget. |
Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts (D19-1)
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Elissa Redmiles, Lisa Maszkiewicz, Emily Hwang, Dhruv Kuchhal, Everest Liu, Miraida Morales, Denis Peskov, Sudha Rao, Rock Stevens, Kristina Gligorić, Sean Kross, Michelle Mazurek, Hal Daumé III
| Challenge: | Despite this, we lack a thorough understanding of how to validly measure readability at scale, especially for domain-specific texts. |
| Approach: | They present a comparison of the validity of well-known readability measures and introduce a novel approach to measure readability at scale. |
| Outcome: | The proposed approach addresses shortcomings of existing measures. |
Attention to Non-Adopters (2026.findings-acl)
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Kaitlyn Zhou, Kristina Gligorić, Myra Cheng, Michelle S. Lam, Vyoma Raman, Boluwatife Aminu, Caeley Woo, Michael Brockman, Hannah Cha, Dan Jurafsky
| Challenge: | incorporating non-adopter perspectives is essential for developing useful and capable LLMs, argues a new study. |
| Approach: | They argue that incorporating non-adopter perspectives is essential for developing broadly useful and capable LLMs. |
| Outcome: | The proposed method will risk missing tasks prioritized by non-adopters, the authors argue . they show that non-dots diverge from those of current users, and non-no-acopter needs point towards novel reasoning tasks. |