Papers by Nicole Meister
Benchmarking Distributional Alignment of Large Language Models (2025.naacl-long)
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| Challenge: | Language models are increasingly being used as simulacra for people, yet their ability to match the distribution of views of a specific demographic group remains uncertain. |
| Approach: | They construct a dataset expanding beyond political values and create human baselines for this task and evaluate the extent to which an LM can align with a particular group’s opinion distribution. |
| Outcome: | The proposed model can better describe opinion distributions than simulate demographic groups. |
MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction (2022.coling-1)
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Amir Pouran Ben Veyseh, Nicole Meister, Seunghyun Yoon, Rajiv Jain, Franck Dernoncourt, Thien Huu Nguyen
| Challenge: | Acronym extraction is the task of identifying acronyms and their expanded forms in texts . existing AE methods for English are limited to specific languages and domains . |
| Approach: | They propose to annotate 27,200 sentences in 6 different languages and 2 new domains for AE. |
| Outcome: | The proposed dataset shows that AE in different languages and learning settings has unique challenges . |