Concatenated Sequence Representation for Multi-Turn Dialogue

Optimizing Dialogue Model Training Efficiency

When training a multi-round dialogue model, an engineer chooses to concatenate an entire K-turn conversation into a single sequence and calculate the loss for all K responses in one forward pass. What is the primary advantage of this approach compared to performing K separate forward passes, where each pass processes an incrementally longer conversational history?

When training a multi-round dialogue model by concatenating the entire conversation into a single sequence for one forward pass, the calculation of the loss for the first model response is influenced by the content of the final user input in the dialogue.