Summary of Piculet: Specialized Models-guided Hallucination Decrease For Multimodal Large Language Models, by Kohou Wang et al.
Piculet: Specialized Models-Guided Hallucination Decrease for MultiModal Large Language Models
by Kohou Wang, Xiang Liu, Zhaoxiang Liu, Kai Wang, Shiguo Lian
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Multimodal Large Language Models (MLLMs) have made significant progress in bridging the gap between visual and language modalities, but still struggle with hallucinations where generated text doesn’t align with image content. To address this challenge without retraining the model, we introduce Piculet, a novel training-free method that enhances input representation by combining descriptions of visual information from multiple specialized models with the original image and query as input to the MLLM. Our evaluation shows that Piculet greatly decreases hallucinations in MLLMs, making it a universal solution that can be easily extended to different MLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make sure language models don’t get confused when combining images with text. Right now, these models often create fake information that doesn’t match the image. To fix this problem without needing to retrain the model, we came up with a method called Piculet. It takes descriptions from multiple specialized models and combines them with the original image and question to give to the language model. This helps reduce the chances of the model creating false information. |
Keywords
» Artificial intelligence » Language model
