63 citations · 215 across the 16 of their papers we have counts for
3 papers · 1 filter
Fine-Tuning Lowers Safety and Disrupts Evaluation Consistency
Kathleen C. Fraser, Hillary Dawkins, Isar Nejadgholi +1
Fine-tuning a general-purpose large language model (LLM) for a specific domain or task has become a routine procedure for ordinary users. However, fine-tuning is known to remove th…
Gender-Neutral Machine Translation Strategies in Practice
Hillary Dawkins, Isar Nejadgholi, Chi-kiu Lo
Gender-inclusive machine translation (MT) should preserve gender ambiguity in the source to avoid misgendering and representational harms. While gender ambiguity often occurs natur…
When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text
Hillary Dawkins, Kathleen C. Fraser, Svetlana Kiritchenko
Detecting AI-generated text is a difficult problem to begin with; detecting AI-generated text on social media is made even more difficult due to the short text length and informal,…