Title: On the Reproducibility of Learned Sparse Retrieval Adaptations for Long Documents Show Chinese title

Title: 关于长文档学习稀疏检索适应的可重复性

Abstract: Document retrieval is one of the most challenging tasks in Information Retrieval. It requires handling longer contexts, often resulting in higher query latency and increased computational overhead. Recently, Learned Sparse Retrieval (LSR) has emerged as a promising approach to address these challenges. Some have proposed adapting the LSR approach to longer documents by aggregating segmented document using different post-hoc methods, including n-grams and proximity scores, adjusting representations, and learning to ensemble all signals. In this study, we aim to reproduce and examine the mechanisms of adapting LSR for long documents. Our reproducibility experiments confirmed the importance of specific segments, with the first segment consistently dominating document retrieval performance. Furthermore, We re-evaluate recently proposed methods -- ExactSDM and SoftSDM -- across varying document lengths, from short (up to 2 segments) to longer (3+ segments). We also designed multiple analyses to probe the reproduced methods and shed light on the impact of global information on adapting LSR to longer contexts. The complete code and implementation for this project is available at: https://github.com/lionisakis/Reproducibilitiy-lsr-long. Show Chinese abstract

Abstract: 文档检索是信息检索中最具挑战性的任务之一。它需要处理更长的上下文，通常会导致更高的查询延迟和计算开销。最近，学习稀疏检索（LSR）作为一种有前景的方法来解决这些挑战。一些研究者提出了通过使用不同的后期方法对LSR方法进行适应以处理更长的文档，包括n-grams和邻近度得分，调整表示，并学习整合所有信号。在本研究中，我们旨在复制并检查适应LSR处理长文档的机制。我们的可重复性实验确认了特定段落的重要性，第一个段落始终主导文档检索性能。此外，我们在不同文档长度上重新评估了最近提出的ExactSDM和SoftSDM方法，从短文档（最多2个段落）到更长的文档（3个以上段落）。我们还设计了多种分析来探究复制的方法，并揭示全局信息对适应LSR处理更长上下文的影响。该项目的完整代码和实现可在以下地址获取：https://github.com/lionisakis/Reproducibilitiy-lsr-long.