เปรียบเทียบวิธี

	Longformer / BigBird	Mixture of Experts	Random Forest
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning	Machine learning
ปีกำเนิด	2020	2017	2001
ผู้ริเริ่ม	Beltagy, Peters & Cohan (Longformer); Zaheer et al. (BigBird)	Shazeer, N. et al.	Breiman, L.
ประเภท	Sparse-attention Transformer for long sequences	Sparse neural network architecture (conditional computation)	Ensemble (bagging of decision trees)
แหล่งต้นตำรับ	Beltagy, I., Peters, M. E. & Cohan, A. (2020). Longformer: The Long-Document Transformer. arXiv.	Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
ชื่อเรียกอื่น	Uzun Dizi Transformer (Longformer / BigBird), uzun dizi transformer, long-document transformer, sparse-attention transformer	Uzman Karışımı (Mixture of Experts — MoE), uzman karışımı, MoE, sparse mixture of experts	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
ที่เกี่ยวข้อง	4	3	4
สรุป	Long-sequence Transformers such as Longformer (Beltagy, Peters & Cohan, 2020) and BigBird (Zaheer et al., 2020) replace the standard Transformer's O(n²) attention with sparse attention patterns that scale linearly, O(n), with sequence length. This lets a single model attend over thousands of tokens — full documents, legal texts, or genomic sequences — that would not fit a conventional Transformer.	Mixture of Experts (MoE) is a sparse neural-network architecture, introduced by Shazeer and colleagues in 2017 with the sparsely-gated MoE layer, in which only a subset of expert sub-networks is activated for each input. As seen in models such as Switch Transformer and Mixtral, it holds computation cost fixed even as the total parameter count grows.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.