A large language model can learn more from a training example than its developer intended. An example meant to teach a coding pattern may also shape how the model handles trust, deception, or harm in unrelated conversations. Researchers call this behavior emergent misalignment, which occurs when training on a narrow undesirable behavior produces broader undesirable behavior that was never explicitly included in the training.
To understand why this happens, it helps to connect three ideas: how models learn, how they generalize beyond their examples, and how they exhibit different kinds of behavior. The term "personality" is used loosely here. It refers to patterns in a model's responses, not to human feelings or consciousness.
LLMs learn behavioral patterns alongside facts. During pretraining, a model repeatedly tries to predict the next piece of text, adjusting its internal parameters to improve those predictions. To do this well, it must learn more than grammar. It must recognize the situation, the speaker's apparent role, and the behavior associated with that role. As a result, a model can absorb patterns linked to traits such as helpfulness, dishonesty, caution, recklessness, kindness, and cruelty.
That knowledge alone does not determine how a model will behave when answering a user. Additional training, including examples of desirable answers and human preferences, shapes the model into an assistant. The model's behavior is therefore partly learned, not a fixed setting that stays unaffected when the model learns something new. Fine-tuning, which trains an already capable model further on a smaller, specialized set of examples, can shift these behaviors in ways developers do not anticipate.
Source: ByteByteGo · Summarized by HeadlinesBriefing