Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs

arXiv:2404.00486v1 Announce Type: new
Abstract: With the rise of large language models (LLMs), ensuring they embody the principles of being helpful, honest, and harmless (3H), known as Human Alignment, becomes crucial. While existing alignment methods like RLHF, DPO, etc., effectively fine-tune LLMs to match preferences in the preference dataset, they often lead LLMs to highly receptive human input and external evidence, even when this information is poisoned. This leads to a tendency for LLMs to be Adaptive Chameleons when external …

abstract alignment arxiv cs.ai cs.cl etc human language language models large language large language models llms match rlhf security threats type