We overcome the limitations of gold-standard passage supervision by distilling relevance from a query-aware context compression model.
We overcome the limitations of gold-standard passage supervision by distilling relevance from a query-aware context compression model.
The model scores each document token with respect to the query. After aggregating these scores into passage-level targets, these scores are used to train an embedding model to retrieve answer passages and supporting passages.