The FGAIF method can be extended to handle other types of hallucinations by incorporating additional fine-grained reward models specifically designed to detect soundness or fluency hallucinations. Similar to the existing reward models for object existence, object attribute, and object relation hallucinations, new reward models can be trained to identify inconsistencies related to soundness or fluency in the generated responses. By collecting AI-based feedback on these specific types of hallucinations at a sub-sentence level, the FGAIF method can be adapted to address a broader range of hallucination issues in large vision-language models.