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cs.LG2026
Contextual Embeddings Beyond the Gold Chunk
Sedigheh Eslami, Markus Krimmel, William Barber +7
Contextual embeddings represent chunks of a document with respect to the document's global context. These models are typically trained and evaluated under the assumption that a rel…
cs.LG2023
Efficient Parallelization Layouts for Large-Scale Distributed Model Training
Johannes Hagemann, Samuel Weinbach, Konstantin Dobler +2
Efficiently training large language models requires parallelizing across hundreds of hardware accelerators and invoking various compute and memory optimizations. When combined, man…