{"ID":2853363,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16393","arxiv_id":"2510.16393","title":"Blending Learning to Rank and Dense Representations for Efficient and Effective Cascades","abstract":"We investigate the exploitation of both lexical and neural relevance signals for ad-hoc passage retrieval. Our exploration involves a large-scale training dataset in which dense neural representations of MS-MARCO queries and passages are complemented and integrated with 253 hand-crafted lexical features extracted from the same corpus. Blending of the relevance signals from the two different groups of features is learned by a classical Learning-to-Rank (LTR) model based on a forest of decision trees. To evaluate our solution, we employ a pipelined architecture where a dense neural retriever serves as the first stage and performs a nearest-neighbor search over the neural representations of the documents. Our LTR model acts instead as the second stage that re-ranks the set of candidates retrieved by the first stage to enhance effectiveness. The results of reproducible experiments conducted with state-of-the-art dense retrievers on publicly available resources show that the proposed solution significantly enhances the end-to-end ranking performance while relatively minimally impacting efficiency. Specifically, we achieve a boost in nDCG@10 of up to 11% with an increase in average query latency of only 4.3%. This confirms the advantage of seamlessly combining two distinct families of signals that mutually contribute to retrieval effectiveness.","short_abstract":"We investigate the exploitation of both lexical and neural relevance signals for ad-hoc passage retrieval. Our exploration involves a large-scale training dataset in which dense neural representations of MS-MARCO queries and passages are complemented and integrated with 253 hand-crafted lexical features extracted from...","url_abs":"https://arxiv.org/abs/2510.16393","url_pdf":"https://arxiv.org/pdf/2510.16393v1","authors":"[\"Franco Maria Nardini\",\"Raffaele Perego\",\"Nicola Tonellotto\",\"Salvatore Trani\"]","published":"2025-10-18T08:16:48Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.LG\",\"cs.PF\"]","methods":"[\"LoRA\"]","has_code":false}