Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation

Despite showing impressive abilities, large language models (LLMs) often struggle with factual inaccuracies, i.e., "hallucinations", even when they hold relevant knowledge. To mitigate these hallucinations, current approaches typically necessitate high-quality human factuality annotations. In this work, we explore Self-Alignment for Factuality, where we leverage the self-evaluation capability of an LLM to provide training signals that steer the model towards factuality. Specifically, we incorporate SELF-EVAL, a self-evaluation component, to prompt an LLM to validate the factuality of its own generated responses solely based on its internal knowledge. Additionally, we design S ¯elf-K ¯nowledge Tuning (SK-TUNING) to augment the LLM's self-evaluation ability by improving the model's confidence estimation and calibration. We then utilize these self-annotated responses to fine-tune the model via Direct Preference Optimization algorithm. We show that the proposed self-alignment approach substantially enhances factual accuracy over LLAMA family models across three key knowledge-intensive tasks on TruthfulQA and BioGEN. We will release our code and data upon acceptance.