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.

翻译：文本排序系统中的一个重要问题是处理构成查询分布尾部的困难查询。这类困难可能源于查询中存在罕见、指定不明确或不完整的查询。在本工作中，我们在不损害其他查询性能的前提下，提升了困难或复杂查询的排序表现。首先，我们利用大语言模型基于相关文档对训练查询进行查询丰富化。接着，仅在丰富后的困难查询上微调专用排序器，而非原始查询。我们将专用排序器与基础排序器的相关性得分相结合，并引入每个查询的查询性能估计分数。本方法不同于现有通常对所有查询使用单一排序器的做法——后者会偏向于构成查询分布主体的简单查询。在DL-Hard数据集上的大量实验表明，采用基于查询性能的原则性评分方法，结合基础排序器与专用排序器，在段落排序任务上相比使用原始查询的基线性能提升高达25%，在文档排序任务上提升高达48.4%，甚至超越了当前最优模型。