10 papers
The Heterogeneous Safety Impacts of Benign Multilingual Fine-Tuning
Will Hawkins, Kaivalya Rawal, Jonathan Rystrøm +8
Fine-tuning a large language model is a ubiquitous method for enhancing its capability on a specific downstream task. However, prior work has shown that this increase in capability…
Grounding Text Embeddings in Stakeholder Associations
Jonathan Rystrøm, Sofie Burgos-Thorsen, Zihao Fu +3
Text embeddings are widely used to analyse large corpora of complex texts. However, it is unclear whether the embeddings capture the same semantic distances as the human experts us…
OxEnsemble: Fair Ensembles for Low-Data Classification
Jonathan Rystrøm, Zihao Fu, Chris Russell
We address the problem of fair classification in settings where data is scarce and unbalanced across demographic groups. Such low-data regimes are common in domains like medical im…
Evaluating the Ability of Explanations to Disambiguate Models in a Rashomon Set
Kaivalya Rawal, Eoin Delaney, Zihao Fu +2
Explainable artificial intelligence (XAI) is concerned with producing explanations indicating the inner workings of models. For a Rashomon set of similarly performing models, expla…
SCALPEL: Selective Capability Ablation via Low-rank Parameter Editing for Large Language Model Interpretability Analysis
Zihao Fu, Xufeng Duan, Zhenguang G. Cai
Large language models excel across diverse domains, yet their deployment in healthcare, legal systems, and autonomous decision-making remains limited by incomplete understanding of…
FairImagen: Post-Processing for Bias Mitigation in Text-to-Image Models
Zihao Fu, Ryan Brown, Shun Shao +3
Text-to-image diffusion models, such as Stable Diffusion, have demonstrated remarkable capabilities in generating high-quality and diverse images from natural language prompts. How…