7 citations · 19 across the 34 of their papers we have counts for
7 papers · 2 filters
Bias after Prompting: Persistent Discrimination in Large Language Models
Nivedha Sivakumar, Natalie Mackraz, Samira Khorshidi +4
A dangerous assumption that can be made from prior work on the bias transfer hypothesis (BTH) is that biases do not transfer from pre-trained large language models (LLMs) to adapte…
Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution
Falaah Arif Khan, Nivedha Sivakumar, Yinong Oliver Wang +5
Large language models (LLMs) have achieved impressive performance, leading to their widespread adoption as decision-support tools in resource-constrained contexts like hiring and a…
Fairness Dynamics During Training
Krishna Patel, Nivedha Sivakumar, Barry-John Theobald +2
We investigate fairness dynamics during Large Language Model (LLM) training to enable the diagnoses of biases and mitigations through training interventions like early stopping; we…
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs
Yinong Oliver Wang, Nivedha Sivakumar, Falaah Arif Khan +6
The recent rapid adoption of large language models (LLMs) highlights the critical need for benchmarking their fairness. Conventional fairness metrics, which focus on discrete accur…
Aligning LLMs by Predicting Preferences from User Writing Samples
Stéphane Aroca-Ouellette, Natalie Mackraz, Barry-John Theobald +1
Accommodating human preferences is essential for creating aligned LLM agents that deliver personalized and effective interactions. Recent work has shown the potential for LLMs acti…
Steering into New Embedding Spaces: Analyzing Cross-Lingual Alignment Induced by Model Interventions in Multilingual Language Models
Anirudh Sundar, Sinead Williamson, Katherine Metcalf +3
Aligned representations across languages is a desired property in multilingual large language models (mLLMs), as alignment can improve performance in cross-lingual tasks. Typically…
ExpertLens: Activation steering features are highly interpretable
Masha Fedzechkina, Eleonora Gualdoni, Sinead Williamson +3
Activation steering methods in large language models (LLMs) have emerged as an effective way to perform targeted updates to enhance generated language without requiring large amoun…