Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| GraphRAG× | Mifumo ya Uenezaji Iliyofichwa× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Kina | Ujifunzaji wa Kina |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2023 | 2022 |
| Mwanzilishi≠ | Yunfan Gao | Robin Rombach |
| Aina≠ | System architecture | Neural network architecture |
| Chanzo asilia≠ | Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., & Wang, M. (2023). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997. link ↗ | Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695). DOI ↗ |
| Majina mbadala≠ | Graph RAG, Knowledge Graph RAG | LDM, Stable Diffusion, Latent Diffusion |
| Zinazohusiana | 4 | 4 |
| Muhtasari≠ | GraphRAG is a retrieval-augmented generation approach that augments large language models with knowledge graphs to improve answer quality and factuality. Rather than retrieving flat text passages, GraphRAG constructs and queries structured knowledge graphs extracted from documents, providing rich contextual information to the language model. | Latent Diffusion Models (LDMs) are a generative approach introduced by Rombach et al. in 2022 that performs the diffusion process in a compressed latent space rather than pixel space, enabling efficient high-resolution image synthesis. By compressing images into a low-dimensional latent representation using a variational autoencoder, diffusion becomes computationally tractable while maintaining visual quality. |
| ScholarGateSeti ya data ↗ |
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