It is possible to fine-tune T5 using a minibatch consisting of different tasks, as long as the tasks share the same input and output format, and the model can distinguish the tasks based on some prefix or label. For example, T5 can be fine-tuned on multiple text-to-text tasks, such as summarization, translation, question answering, etc., by adding a task-specific prefix to the input, such as "summarize:", "translate English to French:", or "answer:". This way, the model can learn to perform different tasks depending on the prefix, and leverage the shared knowledge and vocabulary across tasks ¹.
For GPT-2, it is also possible to fine-tune it using a minibatch consisting of different tasks, but it may be more challenging than for T5. This is because GPT-2 is an auto-regressive language model that only takes the input text as the context and generates the output text token by token, without any explicit separation between input and output. Therefore, it may be harder for GPT-2 to infer the task and the output format from the input text alone, especially if the tasks are diverse and complex. One possible solution is to use a special token or delimiter to mark the end of the input and the beginning of the output, such as "
A powerful language model can enable many applications that require natural language understanding and generation, such as:
Some other possible applications of a powerful language model are:
One possible way to fine-tune a language model to perform text classification by adding additional layers is to add them on top of the language model, after the last hidden layer. This way, the additional layers can leverage the rich contextual representations learned by the language model, and adapt them to the specific task and data. For example, one common additional layer is a linear layer that maps the hidden state of the language model to the output logits, which can be used to predict the class label of the input text. Another possible additional layer is a pooling layer that aggregates the hidden states of the language model over the sequence dimension, which can provide a more compact and global representation of the input text. For example, some pooling methods are max pooling, mean pooling, or attention pooling ¹.
Some possible limitations of modeling with decoder-only Transformers for sequence-to-sequence problems are:
Decoder-only Transformers may not be able to fully utilize the information in the input sequence, as they only rely on the encoder-decoder attention mechanism to access the input tokens. This may limit the ability of the model to capture the long-term dependencies and semantic relationships between the input and output sequences, especially if they are very long or complex. Encoder-decoder Transformers, on the other hand, can encode the input sequence into a latent representation using an encoder network, which can provide a more compact and global view of the input sequence for the decoder network.
Decoder-only Transformers may suffer from exposure bias, which means that they may generate inconsistent or unrealistic output sequences due to the discrepancy between training and inference. During training, decoder-only Transformers use the ground-truth output tokens as inputs for the next time step, while during inference, they use their own predictions as inputs. This may cause the model to deviate from the true data distribution and produce errors that accumulate over time. Encoder-decoder Transformers can mitigate this problem by using teacher forcing, which means that they randomly replace some of the decoder inputs with the ground-truth output tokens during training, to make the model more robust to its own errors.
Decoder-only Transformers may have difficulty in handling multiple tasks or domains, as they have to share the same decoder network for different types of input and output sequences. This may cause interference or confusion for the model, as it has to learn different vocabularies, grammars, and styles for different tasks or domains. Encoder-decoder Transformers can overcome this challenge by using task-specific or domain-specific encoder or decoder networks, which can adapt to different input and output formats and requirements. For example, T5 ¹ uses a text-to-text framework that can handle multiple natural language processing tasks by adding a task-specific prefix to the input sequence, such as "translate English to French:" or "summarize:".
(1) SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers. https://arxiv.org/abs/2212.10325.
(2) SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers. https://arxiv.org/pdf/2212.10325.pdf.
(3) NLP From Scratch: Translation with a Sequence to Sequence .... https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html.
(4) undefined. https://doi.org/10.48550/arXiv.2212.10325.