2 papers
cs.SE2026
Recall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval
Leonardo Venuta, Francesco Tosoni, Paolo Ferragina
Semantic code search and clone detection are essential for software development, maintenance, and reuse. This paper evaluates the effectiveness, efficiency, and scalability of cont…
cs.IR2025
Investigating the Scalability of Approximate Sparse Retrieval Algorithms to Massive Datasets
Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli +2
Learned sparse text embeddings have gained popularity due to their effectiveness in top-k retrieval and inherent interpretability. Their distributional idiosyncrasies, however, hav…