  Alignment is a standard procedure to fine-tune pre-trained large language
models (LLMs) to follow natural language instructions and serve as helpful AI
assistants. We have observed, however, that the conventional alignment process
fails to enhance the factual accuracy of LLMs, and often leads to the
generation of more false facts (i.e. hallucination). In this paper, we study
how to make the LLM alignment process more factual, by first identifying
factors that lead to hallucination in both alignment steps:\ supervised
fine-tuning (SFT) and reinforcement learning (RL). In particular, we find that
training the LLM on new knowledge or unfamiliar texts can encourage
hallucination. This makes SFT less factual as it trains on human labeled data
that may be novel to the LLM. Furthermore, reward functions used in standard RL
can also encourage hallucination, because it guides the LLM to provide more
helpful responses on a diverse set of instructions, often preferring longer and
more detailed responses. Based on these observations, we propose
factuality-aware alignment, comprised of factuality-aware SFT and
factuality-aware RL through direct preference optimization. Experiments show
that our proposed factuality-aware alignment guides LLMs to output more factual
responses while maintaining instruction-following capability.
