让检索的段落相互配合，提升复杂问题回答效果
Beyond Independent Passages: Adaptive Passage Combination Retrieval for Retrieval Augmented Open-Domain Question Answering
- 把多个段落当整体检索，考虑它们之间的关联性
- 在多跳问答任务上准确率显著提升，最高增益达8.2%
- 无需额外停止模块，自动决定该取多少段落
检索增强生成（RAG）通过在推理时引入外部文档，使大语言模型（LLMs）能获取最新知识而无需昂贵重训。但传统RAG方法独立检索段落，常导致冗余、噪声或多样性不足，尤其在嘈杂语料和多跳问题中表现不佳。为此，我们提出自适应段落组合检索（AdaPCR），一种针对黑盒大模型的开放域问答新框架。AdaPCR将段落组合视为检索与重排单位，显式建模段落间依赖关系，包含基于拼接段落的上下文感知查询重构，以及以下游答案可能性为目标的重排训练。关键在于，AdaPCR可自适应选择检索段落数量，无需额外停止模块。在多个QA基准上的实验表明，相比基线方法，AdaPCR在多跳推理任务中表现更优，验证了建模段落间依赖对检索效果的提升作用。
原文摘要 · Abstract (English)
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external documents at inference time, enabling up-to-date knowledge access without costly retraining. However, conventional RAG methods retrieve passages independently, often leading to redundant, noisy, or insufficiently diverse context-particularly problematic - particularly problematic in noisy corpora and for multi-hop questions. To address this, we propose Adaptive Passage Combination Retrieval (AdaPCR), a novel framework for open-domain question answering with black-box LMs. AdaPCR explicitly models dependencies between passages by considering passage combinations as units for retrieval and reranking. It consists of a context-aware query reformulation using concatenated passages, and a reranking step trained with a predictive objective aligned with downstream answer likelihood. Crucially, AdaPCR adaptively selects the number of retrieved passages without additional stopping modules. Experiments across several QA benchmarks show that AdaPCR outperforms baselines, particularly in multi-hop reasoning, demonstrating the effectiveness of modeling inter-passage dependencies for improved retrieval.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。