3 citations · 8 across the 12 of their papers we have counts for
8 papers · 1 filter
Cultural Authenticity: Comparing LLM Cultural Representations to Native Human Expectations
Erin MacMurray van Liemt, Aida Davani, Sinchana Kumbale +2
Cultural representation in Large Language Model (LLM) outputs has primarily been evaluated through the proxies of cultural diversity and factual accuracy. However, a crucial gap re…
Risks of Cultural Erasure in Large Language Models
Rida Qadri, Aida M. Davani, Kevin Robinson +1
Large language models are increasingly being integrated into applications that shape the production and discovery of societal knowledge such as search, online education, and travel…
D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluation
Aida Mostafazadeh Davani, Mark Díaz, Dylan Baker +1
While human annotations play a crucial role in language technologies, annotator subjectivity has long been overlooked in data collection. Recent studies that have critically examin…
GeniL: A Multilingual Dataset on Generalizing Language
Aida Mostafazadeh Davani, Sagar Gubbi, Sunipa Dev +2
Generative language models are transforming our digital ecosystem, but they often inherit societal biases, for instance stereotypes associating certain attributes with specific ide…
SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models
Akshita Jha, Aida Davani, Chandan K. Reddy +3
Stereotype benchmark datasets are crucial to detect and mitigate social stereotypes about groups of people in NLP models. However, existing datasets are limited in size and coverag…
The Moral Foundations Reddit Corpus
Jackson Trager, Alireza S. Ziabari, Elnaz Rahmati +15
Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, environmental action, political engagement, and protest. Various computational…
Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations
Aida Mostafazadeh Davani, Mark Díaz, Vinodkumar Prabhakaran
Majority voting and averaging are common approaches employed to resolve annotator disagreements and derive single ground truth labels from multiple annotations. However, annotators…
On Releasing Annotator-Level Labels and Information in Datasets
Vinodkumar Prabhakaran, Aida Mostafazadeh Davani, Mark Díaz
A common practice in building NLP datasets, especially using crowd-sourced annotations, involves obtaining multiple annotator judgements on the same data instances, which are then…