Problem

The Problem of LLM Misalignment

LLM misalignment occurs when a model's output fails to match the specific goals or contexts intended by the user. This issue frequently arises because pre-trained models are not inherently trained to follow instructions, leading to unintended behaviors such as the generation of harmful content or the perpetuation of biases present in their training data. Resolving these misalignment challenges is essential to ensure that a model's outputs are not only accurate and relevant, but also ethically sound and non-discriminatory.

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Updated 2026-04-30

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Ch.4 Alignment - Foundations of Large Language Models

Foundations of Large Language Models

Foundations of Large Language Models Course

Computing Sciences