Abstract Concept erasure aims to remove specified features from a representation. It can
improve fairness (eg preventing a classifier from using gender or race) and interpretability …

Large language models (LLMs) often exhibit undesirable behaviours, such as generating
untruthful or biased content. Editing their internal representations has been shown to be …

Methods for erasing human-interpretable concepts from neural representations that assume
linearity have been found to be tractable and useful. However, the impact of this removal on …

We describe a simple and effective method (Spectral Attribute removaL; SAL) to remove
private or guarded information from neural representations. Our method uses matrix …

Fusing multiple modalities has proven effective for multimodal information processing.
However, the incongruity between modalities poses a challenge for multimodal fusion …

We introduce a task consisting in matching a proof to a given mathematical statement. The
task fits well within current research on Mathematical Information Retrieval and, more …

Low-rank approximations, of the weight and feature space can enhance the performance of
deep learning models, whether in terms of improving generalization or reducing the latency …

Ensuring fairness in NLP models is crucial, as they often encode sensitive attributes like
gender and ethnicity, leading to biased outcomes. Current concept erasure methods attempt …

The burgeoning field of Natural Language Processing (NLP) stands at a critical juncture
where the integration of fairness within its frameworks has become an imperative. This PhD …

When translating from notional gender languages (eg, English) into grammatical gender
languages (eg, Italian), the generated translation requires explicit gender assignments for …