Divine Benevolence Explains Everything

4 Conclusions We have extended the GLU family of layers and proposed their use in Transformer. In a transfer-learning setup, the new variants seem to produce better perplexities for the de-noising objective used in pre-training, as well as better results on many downstream language-understanding tasks. These architectures are simple to implement, and have no apparent computational drawbacks. We offer no explanation as to why these architectures seem to work; we attribute their success, as all else, to divine benevolence. 2 Each training step took approximately 0.15 seconds on a 32-core TPUv2 cluster. 3 This departs from [BaEfel et al., 2019], who fine-tuned separately on the different tasks. We chose one fine-tuning run for simplicity.

A screenshot of an academic paper’s “Conclusions” section discusses improved neural-network architectures and then openly admits that the authors cannot explain why they work, attributing the success to “divine benevolence.” The highlighted sentence is used as a sarcastic reaction to unexplained success, hand-waving, or results that seem too good to justify. objects

• @gentoomemes / 16404 2025-03-13