  An important problem in text-ranking systems is handling the hard queries
that form the tail end of the query distribution. The difficulty may arise due
to the presence of uncommon, underspecified, or incomplete queries. In this
work, we improve the ranking performance of hard or difficult queries without
compromising the performance of other queries. Firstly, we do LLM based query
enrichment for training queries using relevant documents. Next, a specialized
ranker is fine-tuned only on the enriched hard queries instead of the original
queries. We combine the relevance scores from the specialized ranker and the
base ranker, along with a query performance score estimated for each query. Our
approach departs from existing methods that usually employ a single ranker for
all queries, which is biased towards easy queries, which form the majority of
the query distribution. In our extensive experiments on the DL-Hard dataset, we
find that a principled query performance based scoring method using base and
specialized ranker offers a significant improvement of up to 25% on the passage
ranking task and up to 48.4% on the document ranking task when compared to the
baseline performance of using original queries, even outperforming SOTA model.
