Training LLMs on knowingly false data induces broad misalignment
Original title: Knowingly Wrong: Training LLMs Against Their Own Beliefs can Induce Misalignment
Fine-tuning LLMs on data they believe to be false induces broad misalignment across domains even without harmful content.
Fine-tuning on harmful data can make LLMs misaligned. We show that fine-tuning on data the model believes false but harmless can also induce misalignment. In mathematics, misaligned answers rise from 4% to over 60% as training answers become more wrong. Misalignment comes from internal inconsistency where model states wrong answers but probabilities prefer true ones.
- Misaligned answers in mathematics rise from 4% to over 60% with more wrong training data
- Model states wrong answers but multiple-choice probabilities still prefer true ones
- Single fact updated in knowledge editing leads to misalignment if model still believes true fact
- Content filters cannot distinguish framings that lead to different misalignment levels
- AI models
- LLM
From the original article
1 min read at arxiv.orgFine-tuning a large language model (LLM) on harmful data in a narrow domain, such as bad medical advice, can make it broadly misaligned. How realistic this risk is remains unclear, since models are very rarely fine-tuned on explicitly harmful data in practice. In this work, we show that fine-tuning on data the model believes to be false but that has no harmful content, can also induce broad misalignment.
By Ariana M. Villegas-Suarez · arxiv.org
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