  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.
