BlendFilter：通过查询生成融合与知识过滤推进检索增强大型语言模型
BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering
AI总结：
针对检索增强LLMs在复杂输入和噪声检索下效果受限的问题，提出BlendFilter方法，通过融合内外部知识的查询生成与利用LLM内在能力的知识过滤模块，在三个问答基准上显著超越基线。
摘要：
检索增强大型语言模型（LLMs）在提升知识密集型场景性能方面具有显著优势。然而，这些方法在处理复杂输入时常常面临挑战，并因噪声知识检索而遇到困难，明显阻碍了模型的有效性。为了解决这一问题，我们引入了BlendFilter，这是一种通过将查询生成融合与知识过滤相结合来提升检索增强LLMs的新颖方法。BlendFilter通过其查询生成方法提出融合过程，该方法将外部和内部知识增强与原始查询相整合，确保全面的信息收集。此外，我们独特的知识过滤模块利用LLM的内在能力，有效消除了无关数据。我们在三个开放域问答基准上进行了广泛实验，结果清楚地表明，我们创新的BlendFilter显著超越了最先进的基线。
英文摘要：
Retrieval-augmented Large Language Models (LLMs) offer substantial benefits in enhancing performance across knowledge-intensive scenarios. However, these methods often face challenges with complex inputs and encounter difficulties due to noisy knowledge retrieval, notably hindering model effectiveness. To address this issue, we introduce BlendFilter, a novel approach that elevates retrieval-augmented LLMs by integrating query generation blending with knowledge filtering. BlendFilter proposes the blending process through its query generation method, which integrates both external and internal knowledge augmentation with the original query, ensuring comprehensive information gathering. Additionally, our distinctive knowledge filtering module capitalizes on the intrinsic capabilities of the LLM, effectively eliminating extraneous data. We conduct extensive experiments on three open-domain question answering benchmarks, and the findings clearly indicate that our innovative BlendFilter surpasses state-of-the-art baselines significantly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
