Editing Language Models with Natural Language Feedback

Even the most sophisticated language models are not immune to inaccuracies, bias or becoming obsolete, highlighting the need for efficient model editing. Model editing involves altering a model’s knowledge or representations to achieve specific outcomes without the need for extensive retraining. Traditional research has focused on editing factual data within a narrow scope—limited to knowledge triplets like ‘subject-object-relation.’ Yet, as language model applications broaden, so does the necessity for diverse editing approaches. In this talk, I will describe our work that introduces a novel dataset where edit requests are natural language sequences, expanding the editing capabilities beyond factual adjustments to encompass a more comprehensive suite of modifications including bias mitigation. This development not only enhances the precision of language models but also increases their adaptability to evolving information and application demands.