SPARK: Adaptive Parametric Knowledge Participation for Faithful Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) enhances the faithfulness of large language models (LLMs) by incorporating external knowledge into the generation process. However, conflicts between retrieved context and parametric knowledge can cause LLMs to rely on inconsistent internal knowledge, hindering their ability to faithfully leverage retrieved evidence. Existing approaches mitigate this issue by editing parametric knowledge to resolve such conflicts. However, locating and modifying knowledge encoded in model parameters is challenging and may inadvertently compromise the backbone LLM's capabilities. In this paper, we introduce SPARK, a novel framework that formulates parametric knowledge c...
| Authors | Ruochang Li, Pengcheng Huang, Zhenghao Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 June 2026 |
| Last updated at source | 13 October 2026 |
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