DeBERTa improves BERT-style models by separating content and relative position in attention and adding absolute positions to the decoder, yielding consistent gains on NLU and NLG tasks and the first single-model superhuman score on SuperGLUE.
Race: Large-scale reading comprehension dataset from examinations
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DeBERTaV3 improves DeBERTa by switching to replaced token detection pre-training and using gradient-disentangled embedding sharing, reaching 91.37% on GLUE and new SOTA on XNLI zero-shot.
Empirical tests show LLMs from 1B to 7B parameters exhibit catastrophic forgetting during continual instruction tuning, with forgetting severity increasing with scale and decoder-only models retaining more than encoder-decoder models.
citing papers explorer
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DeBERTa: Decoding-enhanced BERT with Disentangled Attention
DeBERTa improves BERT-style models by separating content and relative position in attention and adding absolute positions to the decoder, yielding consistent gains on NLU and NLG tasks and the first single-model superhuman score on SuperGLUE.
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DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
DeBERTaV3 improves DeBERTa by switching to replaced token detection pre-training and using gradient-disentangled embedding sharing, reaching 91.37% on GLUE and new SOTA on XNLI zero-shot.
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An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
Empirical tests show LLMs from 1B to 7B parameters exhibit catastrophic forgetting during continual instruction tuning, with forgetting severity increasing with scale and decoder-only models retaining more than encoder-decoder models.