6 accepted papers
Large language models (LLMs) can be enhanced with external knowledge through two dominant approaches: (1) **retrieval-augmented generation (RAG)**, which supplements LLMs with in-context retrieved passages, and (2) **parametric knowledge adaptation (PKA)**, which directly updates model parameters wi…
Typically, parametric adaptation methods such as domain-adaptive pretraining (DAP) and retrieval-augmented generation (RAG) have been considered effective approaches for adapting large language models (LLMs) to new knowledge or domains. To unify positive effects of parametric adaptation and RAG, thi
Model knowledge editing enables the efficient correction of erroneous information and the continuous updating of outdated knowledge within language models. While existing research has demonstrated strong performance in single-instance or few-instance sequential editing and one-time massive editing s…
Model knowledge editing has become a widely researched topic because it enables efficient and rapid injection of new knowledge into language models or the correction of erroneous or outdated knowledge. Existing model knowledge editing methods typically categorized into single-instance sequential edi…
Among the recently emerged knowledge editing methods, in-context knowledge editing (IKE) has shown respectable abilities on knowledge editing in terms of generalization and specificity. Noting the promising advantages but unexplored issues of IKE, we propose **DistillMIKE** as a novel extension of I…
Generative retrieval, which maps from a query to its relevant document identifiers (docids), has recently emerged as a new information retrieval (IR) paradigm, however, having suffered from 1) the $\textit{lack of the intermediate reasoning step}$, caused by the manner of merely using a query to pe…