Piculet: Specialized Models-Guided Hallucination Decrease for MultiModal Large Language Models
This addresses hallucinations in MLLMs for users needing reliable multimodal outputs, but it is incremental as it builds on existing methods without retraining.
The paper tackles the problem of hallucinations in Multimodal Large Language Models (MLLMs) by introducing Piculet, a training-free method that uses specialized models to extract visual descriptions and combine them with the original input, resulting in a significant decrease in hallucinations.
Multimodal Large Language Models (MLLMs) have made significant progress in bridging the gap between visual and language modalities. However, hallucinations in MLLMs, where the generated text does not align with image content, continue to be a major challenge. Existing methods for addressing hallucinations often rely on instruction-tuning, which requires retraining the model with specific data, which increases the cost of utilizing MLLMs further. In this paper, we introduce a novel training-free method, named Piculet, for enhancing the input representation of MLLMs. Piculet leverages multiple specialized models to extract descriptions of visual information from the input image and combine these descriptions with the original image and query as input to the MLLM. We evaluate our method both quantitively and qualitatively, and the results demonstrate that Piculet greatly decreases hallucinations of MLLMs. Our method can be easily extended to different MLLMs while being universal.