5 citations · 5 across the 10 of their papers we have counts for
11 papers
Settle: Learning When to Stop Reasoning
Ryan Brown, Zihao Fu, Chris Russell
Reasoning models often continue generating after their answers have settled. Settle learns when to stop from answer stability in completed traces. It trains the existing end-of-rea…
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…
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…
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…
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…
CAST: Compositional Analysis via Spectral Tracking for Understanding Transformer Layer Functions
Zihao Fu, Ming Liao, Chris Russell +1
Large language models have achieved remarkable success but remain largely black boxes with poorly understood internal mechanisms. To address this limitation, many researchers have…
Multi-use LLM Watermarking and the False Detection Problem
Zihao Fu, Chris Russell
Digital watermarking is a promising solution for mitigating some of the risks arising from the misuse of automatically generated text. These approaches either embed non-specific wa…
Evaluating Model Explanations without Ground Truth
Kaivalya Rawal, Zihao Fu, Eoin Delaney +1
There can be many competing and contradictory explanations for a single model prediction, making it difficult to select which one to use. Current explanation evaluation frameworks…
OxonFair: A Flexible Toolkit for Algorithmic Fairness
Eoin Delaney, Zihao Fu, Sandra Wachter +2
We present OxonFair, a new open source toolkit for enforcing fairness in binary classification. Compared to existing toolkits: (i) We support NLP and Computer Vision classification…