A 3 E: Towards Compositional Model Editing

Model editing has become a *de-facto* practice to address hallucinations and outdated knowledge of large language models (LLMs). However, existing methods are predominantly evaluated in isolation, i.e., one edit at a time, failing to consider a critical scenario of compositional model editing, where multiple edits must be integrated and jointly utilized to answer real-world multifaceted questions. For instance, in medical domains, if one edit informs LLMs that COVID-19 causes fever and another that it causes loss of taste, a qualified compositional editor should enable LLMs to answer the question What are the symptoms of COVID-19?