On the Representation Collapse of Sparse Mixture of Experts - Chi, Zewen; Dong, Li; Huang, Shaohan; Dai, Damai; Ma, Shuming; Patra, Barun; Singhal, Saksham; Bajaj, Payal; Song, Xia; Wei, Furu (2022 Neural Information Processing Systems, 2022)
Sparse mixture of experts provides larger model capacity while requiring a constant computational overhead. It employs the routing mechanism to distribute input tokens to the best-matched experts according to their hidden representations. However, learning such a routing mechanism encourages token clustering around expert centroids, implying a trend toward representation collapse. In this work, we propose to estimate the routing scores between tokens and experts on a low-dimensional hypersphere. We conduct extensive experiments on cross-lingual language model pre-training and ﬁne-tuning on downstream tasks. Experimental results across seven multilingual benchmarks show that our method achieves consistent gains. We also present a comprehensive analysis on the representation and routing behaviors of our models. Our method alleviates the representation collapse issue and achieves more consistent routing than the baseline mixture-of-experts methods.