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For tasks involving multiple text segments, a common input formatting strategy is to concatenate the sequences and use a special separator token, like `[SEP]`, to distinguish between them. For instance, an input sequence $$x_m$$ and a target sequence $$y_1, y_2, \\dots, y_n$$ can be combined into a single input stream for a model as: $$x_m$$ `[SEP]` $$y_1 y_2 \\dots y_n$$ `[SEP]`.

Input Formatting with Separator Tokens

Analyze the following scenario and identify the most likely problem with the model's input, then propose a specific solution.

Troubleshooting Model Input Formatting

A developer is training a model for a question-answering task. The input consists of a context paragraph followed by a question. The developer simply concatenates the context and the question text into a single sequence without any special markers between them. The model is struggling to learn the task effectively. Based on common input formatting practices, what is the most likely reason for the model's poor performance, and how should the input be modified to address this issue?

Diagnosing Input Formatting Errors

A language model needs to process two distinct sentences as a single input to determine if they are paraphrases of each other. The first sentence is 'The team celebrated their victory.' and the second is 'The squad rejoiced after their win.'. Based on the common method for handling multiple text segments, which of the following represents the correctly formatted input?

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