AI 中文总结
研究重现Cuconasu等人关于噪声对检索增强生成系统问答性能影响的发现，在扩展实验设置下评估其稳健性，发现该效应受推理配置影响大，强调审视推理设计的重要性。
AI 中文摘要
近期研究表明向检索增强生成（RAG）系统输入中添加无关文档可提升问答性能，即‘噪声能力’。本文重现相关主要发现并在扩展实验设置下评估其稳健性。先确认原设置下该现象存在，后通过系列扩展探究噪声效应根源，发现其对推理配置敏感，结合错误分析表明原效应在当前条件下不能确认为噪声检索的普遍益处，强调审视推理设计的重要性。代码可通过链接获取。
英文摘要
Recent work has suggested that adding irrelevant documents to the input of retrieval-augmented generation (RAG) systems can improve question-answering performance, a phenomenon referred to as the Power of Noise. This motivated investigations into the role of noise in information retrieval. In this paper, we reproduce the main findings of Cuconasu et al. and evaluate the robustness of the effect under extended experimental settings. We first confirm that the phenomenon holds under the original setup, which uses earlier-generation LLMs, restrictive prompting and constrained decoding settings. We subsequently introduce a series of extensions to investigate the underlying causes of the noise effect, examining the authors' original design choices including the use of different models, instruction prompting, and relaxed output length constraints. Across these ablations, the Power-of-Noise pattern proves highly sensitive to inference configuration: it can appear, weaken, or disappear under small changes to prompt formulation and decoding limits. Combined with our error analysis, which shows substantial contributions from truncation and malformed generations, this variance indicates that the original effect cannot be robustly confirmed as a general benefit of noisy retrieval under these experimental conditions. More broadly, our work highlights the importance of carefully scrutinizing inference design in retrieval-augmented generation systems. Our code is available at https://github.com/ina0105/The-Power-of-Noise-Reproduction.
CommentsSIGIR 26 Repro
