Understanding Streamlined Transformers: Employing Clear Language in Top-tier NLP - Installment 5: Decoder and Beyond
Transformer-based language models have revolutionized the field of natural language processing, and a key technique that contributes to their success is Teacher Forcing. This method, as we will explore, accelerates training by providing the decoder with the correct tokens during training, rather than relying on the model's own predictions.
At the heart of the Transformers architecture lies the Masked Multi-Head Attention layer, which calculates attention scores using the query and key matrices from the encoder and the values matrix from the decoder. Following this layer, a Feed-Forward + Add & Norm layer is employed, as explained in the series.
The decoder in Transformer models converts inputs into mathematical notations, then translates them back into the requested output. During training, Teacher Forcing simplifies the learning problem by providing the decoder with the actual next token from the training data at each time step, rather than the token the model predicted in the previous step. This direct supervision eliminates the need for the model to recover from its own mistakes within the same training pass.
Teacher Forcing reduces error propagation and variance in the decoder's context, which helps the model converge faster compared to autoregressive training where predicted tokens are used as inputs. This technique is especially effective in stabilizing training and is widely used in training sequence-to-sequence Transformer models for language generation tasks like translation and fine-tuning large language models.
While Teacher Forcing improves training speed and stability, it is not without its challenges. Some research has pointed out potential pitfalls, such as the learned model possibly failing at test time when it no longer has access to ground truth tokens (exposure bias). However, the core benefit remains accelerated and more efficient training by conditioning on true sequences rather than predicted ones during training.
The Transformers architecture processes user prompts, adds positional encodings, and uses a Masked Multi-Head Attention layer before generating a probability distribution for each word in the vocabulary. The chosen word is output as an index in the vocabulary. The encoder converts the user prompt into embeddings and adds positional encoding. The decoder prepends the ground truth with a token indicating the first token of the sentence.
During Masked Multi-Head Attention, future words are hidden by adding extremely negative numbers to their attention scores, causing them to be ignored in the next stage. This ensures that the model only uses the predicted words from the previous steps during training. The Values matrix, created by a Masked Multi-Head Attention layer in the decoder, continues to the next stage after Masked Multi-Head Attention.
The final decoder in the stack passes its output through a linear layer, which generates as many numerical representations as the size of the vocabulary. The Softmax layer is used to convert these numerical representations into probabilities for each word in the vocabulary, with the highest probability being the chosen word.
This article is the fourth in a series that delves into the inner workings of Transformer-based language models. The fourth part focuses on DecoderTransformers, an Encoder-Decoder architecture that underpins the Transformers' ability to generate coherent and contextually accurate responses.
Artificial Intelligence, a key component in Transformer-based language models, is harnessed through the decoder, which converts inputs into mathematical notations and translates them back into the requested output. This decoder employs Teacher Forcing during training, a technique that provides the actual next token from the training data at each time step, rather than relying on the model's own predictions, thus leveraging the power of technology for more efficient and accurate training.
