Short Answer

Analysis of a Simplified Objective Function

Consider the objective function U(x,y;θ)=t=1TA(x,yt,y<t)logπθ(ytx,y<t)U(\mathbf{x}, \mathbf{y}; \theta) = \sum_{t=1}^{T} A(\mathbf{x}, y_t, \mathbf{y}_{<t}) \log \pi_\theta(y_t|\mathbf{x}, \mathbf{y}_{<t}) used for training a sequence generation model. Analyze what this objective function simplifies to if the weighting function A()A(\cdot) is set to a constant value of 1 for all steps tt. How does this simplified objective relate to the standard training objective of a typical autoregressive language model?

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Updated 2025-10-07

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Ch.4 Alignment - Foundations of Large Language Models

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