What characterizes an autoregressive model in Generative AI?

What characterizes an autoregressive model in Generative AI?

• A. Generating all outputs simultaneously
• B. Using prior outputs as input for subsequent generation
• C. Combining multiple models for a single output
• D. Evaluating outputs based on user feedback

Answer: (B)

An autoregressive model in Generative AI is characterized by its process of using prior outputs as input for subsequent generations. This means that the model generates one piece of data at a time and then incorporates that piece into the input for generating the next piece. For example, in text generation, the model might generate the first word, and then that word becomes part of the context for generating the second word, and so on. This sequential dependency allows the model to create coherent and contextually aware outputs. In contrast, generating all outputs simultaneously refers to models that produce a complete output in one go, which is not a feature of autoregressive models. Similarly, combining multiple models for a single output does not align with the autoregressive nature, as it implies collaboration rather than a stepwise generation process. Evaluating outputs based on user feedback is commonly associated with reinforcement learning or other iterative improvement strategies, which, while relevant to Generative AI, is not a characteristic feature of autoregressive models.

An autoregressive model in Generative AI is characterized by its process of using prior outputs as input for subsequent generations. This means that the model generates one piece of data at a time and then incorporates that piece into the input for generating the next piece. For example, in text generation, the model might generate the first word, and then that word becomes part of the context for generating the second word, and so on. This sequential dependency allows the model to create coherent and contextually aware outputs.

In contrast, generating all outputs simultaneously refers to models that produce a complete output in one go, which is not a feature of autoregressive models. Similarly, combining multiple models for a single output does not align with the autoregressive nature, as it implies collaboration rather than a stepwise generation process. Evaluating outputs based on user feedback is commonly associated with reinforcement learning or other iterative improvement strategies, which, while relevant to Generative AI, is not a characteristic feature of autoregressive models.