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This technical report briefly describes our JDExplore d-team's submissionVega v1 on the General Language Understanding Evaluation (GLUE) leaderboard,where GLUE is a collection of nine natural language understanding tasks,including question answering, linguistic acceptability, sentiment analysis,text similarity, paraphrase detection, and natural language inference. [Method]We investigate several effective strategies and choose their best combinationsetting as the training recipes. As for model structure, we employ the vanillaTransformer with disentangled attention as the basic block encoder. Forself-supervised training, we employ the representative denoising objective(i.e., replaced token detection) in phase 1 and combine the contrastiveobjective (i.e., sentence embedding contrastive learning) with it in phase 2.During fine-tuning, several advanced techniques such as transductivefine-tuning, self-calibrated fine-tuning, and adversarial fine-tuning areadopted. [Results] According to our submission record (Jan. 2022), with ouroptimized pretraining and fine-tuning strategies, our 1.3 billion model setsnew state-of-the-art on 4/9 tasks, achieving the best average score of 91.3.Encouragingly, our Vega v1 is the first to exceed powerful human performance onthe two challenging tasks, i.e., SST-2 and WNLI. We believe our empiricallysuccessful recipe with a bag of tricks could shed new light on developingefficient discriminative large language models.

Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE