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Found 5 papers, 2 papers with code
Robust Pronoun Use Fidelity with English LLMs: Are they Reasoning, Repeating, or Just Biased?
We find that while models can mostly faithfully reuse previously-specified pronouns in the presence of no distractors, they are significantly worse at processing she/her/her, singular they and neopronouns.
What explains the success of cross-modal fine-tuning with ORCA?
ORCA (Shen et al., 2023) is a recent technique for cross-modal fine-tuning, i. e., applying pre-trained transformer models to modalities beyond their training data.
The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis
Compared to work on monolingual (English) in-context learning, multilingual in-context learning is under-explored, and we lack an in-depth understanding of the role of demonstrations in this context.
A Lightweight Method to Generate Unanswerable Questions in English
If a question cannot be answered with the available information, robust systems for question answering (QA) should know _not_ to answer.
Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness
Through a critical review of how intersectionality is discussed in 30 papers from the AI fairness literature, we deductively and inductively: 1) map how intersectionality tenets operate within the AI fairness paradigm and 2) uncover gaps between the conceptualization and operationalization of intersectionality.
