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cs.IR2026
Efficient Listwise Reranking with Compressed Document Representations
Hervé Déjean, Stéphane Clinchant
Reranking, the process of refining the output from a first-stage retriever, is often considered computationally expensive, especially when using Large Language Models (LLMs). A com…
cs.IR2026
On the Challenges and Opportunities of Learned Sparse Retrieval for Code
Simon Lupart, Maxime Louis, Thibault Formal +2
Retrieval over large codebases is a key component of modern LLM-based software engineering systems. Existing approaches predominantly rely on dense embedding models, while learned…
cs.IR2025
Reranking with Compressed Document Representation
Hervé Déjean, Stéphane Clinchant
Reranking, the process of refining the output of a first-stage retriever, is often considered computationally expensive, especially with Large Language Models. Borrowing from recen…