1Cademy - Multi-Round Prediction Problem

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Formal Definition of LLM Inference

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Multi-Round Prediction Problem

A multi-round prediction problem is a scenario where a Large Language Model engages in a dialogue over multiple turns. In this setting, the model must not only generate a response to the initial input but also incorporate subsequent user inputs that refine or expand upon earlier interactions. A conversation with $K$ rounds can be formally denoted by the sequence $\{\mathbf{x}^1, \mathbf{y}^1, \mathbf{x}^2, \mathbf{y}^2, \dots, \mathbf{x}^K, \mathbf{y}^K\}$ , where $\mathbf{x}^k$ denotes the user request and $\mathbf{y}^k$ denotes the response for each round $k$ . Unlike single-turn interactions, the model must generate responses by processing an incrementally growing conversational history.

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Updated 2026-05-01

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