Craig Macdonald

Adaptive retrieval-augmented generation (RAG) models offer an effective approach for integrating external knowledge. However, existing methods either rely on frozen large language models (LLMs) without explicit supervision or require costly LLM finetuning. Therefore, we propose SPARKLE, a structu...

Multi-vector dense retrieval models, such as ColBERT, achieve strong retrieval effectiveness by modelling fine-grained token-level interactions between queries and documents. Methods such as PLAID use centroid-based quantisation of each token's vector to reduce the index size and speed up retriev...

Dense retrieval uses embedded dense representations for documents to be obtained from language models ingested in vector stores. To reduce large space requirements and/or efficiency concerns, approximate nearest neighbours algorithms can be deployed, typically at the cost of effectiveness. Among...

Traditional search tasks aim to satisfy user information needs by returning a subset of a collection of documents, ranked by the documents' relevance to a user query. However, some collections that contain useful information also contain \emph{sensitive} personal information. Recently, there has...