XLNet, RoBERTa, ALBERT (2019) › Limitations
History of Language AIAll chapters XLNet, RoBERTa, ALBERT (2019) › LimitationsOne section · Sections in this chapter5 / 7 ↓
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Despite their improvements, each model retained limitations and introduced new challenges. XLNet's permutation language modeling, while theoretically elegant, increased computational complexity compared to BERT. The need to sample permutations and maintain two-stream attention made training and inference more expensive. The model required careful implementation to achieve the promised benefits, and the complexity limited its adoption compared to simpler alternatives.
RoBERTa's improvements came primarily from more data and longer training, which required substantial computational resources. The tenfold increase in training data meant that reproducing RoBERTa required access to large datasets and significant compute. While the methodology improvements were valuable, they didn't fundamentally change the model's capabilities or address core limitations like static embeddings.
ALBERT's parameter sharing, while efficient, might have limited the model's representational capacity. All layers learning the same transformations could constrain the model's ability to learn hierarchical representations. Some analysis suggested that ALBERT models might require more training to converge due to the parameter sharing constraint, partially offsetting the efficiency gains.
All three models retained BERT's limitation of producing static embeddings that didn't capture context-dependent meaning variations as flexibly as later models. They also maintained the encoder-only architecture, limiting their ability to generate text naturally. XLNet's autoregressive structure theoretically enabled generation, but the permutation mechanism made generation more complex than in purely autoregressive models.
The models also shared limitations with transformer architectures more broadly. They struggled with very long sequences due to quadratic attention complexity. They required substantial computational resources for training, even with ALBERT's efficiency improvements. They couldn't easily incorporate new information after training without fine-tuning.