BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering
<|reference_start|>BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering: 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.<|reference_end|>
@article{wang2024blendfilter:,
    title={BlendFilter: Advancing Retrieval-Augmented Large Language Models via
  Query Generation Blending and Knowledge Filtering},
    author={Haoyu Wang, Ruirui Li, Haoming Jiang, Jinjin Tian, Zhengyang Wang,
  Chen Luo, Xianfeng Tang, Monica Cheng, Tuo Zhao, Jing Gao},
    journal={arXiv preprint arXiv:2402.11129},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11129},
    primaryClass={cs.CL}
}